novel-classification.ipynb

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    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Methodology\" data-toc-modified-id=\"Methodology-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Methodology</a></span></li><li><span><a href=\"#Train,-Test,-and-Validation-Sets\" data-toc-modified-id=\"Train,-Test,-and-Validation-Sets-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Train, Test, and Validation Sets</a></span><ul class=\"toc-item\"><li><span><a href=\"#Load-train-and-test-data\" data-toc-modified-id=\"Load-train-and-test-data-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Load train and test data</a></span></li><li><span><a href=\"#Split-train-set-into-train-and-validation-sets\" data-toc-modified-id=\"Split-train-set-into-train-and-validation-sets-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Split train set into train and validation sets</a></span></li></ul></li><li><span><a href=\"#Exploratory-Data-Analysis-(EDA)\" data-toc-modified-id=\"Exploratory-Data-Analysis-(EDA)-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Exploratory Data Analysis (EDA)</a></span><ul class=\"toc-item\"><li><span><a href=\"#Class-distribution\" data-toc-modified-id=\"Class-distribution-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Class distribution</a></span></li><li><span><a href=\"#Sentence-length-distribution\" data-toc-modified-id=\"Sentence-length-distribution-3.2\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>Sentence length distribution</a></span></li><li><span><a href=\"#Character-distribution\" data-toc-modified-id=\"Character-distribution-3.3\"><span class=\"toc-item-num\">3.3&nbsp;&nbsp;</span>Character distribution</a></span></li></ul></li><li><span><a href=\"#Word-Embedding\" data-toc-modified-id=\"Word-Embedding-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Word Embedding</a></span></li><li><span><a href=\"#Features-(X)-and-Labels-(Y)\" data-toc-modified-id=\"Features-(X)-and-Labels-(Y)-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Features (X) and Labels (Y)</a></span><ul class=\"toc-item\"><li><span><a href=\"#Tokenization-and-padding\" data-toc-modified-id=\"Tokenization-and-padding-5.1\"><span class=\"toc-item-num\">5.1&nbsp;&nbsp;</span>Tokenization and padding</a></span></li><li><span><a href=\"#Creating-an-embedding-matrix\" data-toc-modified-id=\"Creating-an-embedding-matrix-5.2\"><span class=\"toc-item-num\">5.2&nbsp;&nbsp;</span>Creating an embedding matrix</a></span></li><li><span><a href=\"#Encoding-the-labels\" data-toc-modified-id=\"Encoding-the-labels-5.3\"><span class=\"toc-item-num\">5.3&nbsp;&nbsp;</span>Encoding the labels</a></span></li></ul></li><li><span><a href=\"#Model\" data-toc-modified-id=\"Model-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Model</a></span><ul class=\"toc-item\"><li><span><a href=\"#Define-the-functions-for-ploting\" data-toc-modified-id=\"Define-the-functions-for-ploting-6.1\"><span class=\"toc-item-num\">6.1&nbsp;&nbsp;</span>Define the functions for ploting</a></span></li><li><span><a href=\"#Parameters\" data-toc-modified-id=\"Parameters-6.2\"><span class=\"toc-item-num\">6.2&nbsp;&nbsp;</span>Parameters</a></span></li><li><span><a href=\"#GRU\" data-toc-modified-id=\"GRU-6.3\"><span class=\"toc-item-num\">6.3&nbsp;&nbsp;</span>GRU</a></span><ul class=\"toc-item\"><li><span><a href=\"#Define-the-model\" data-toc-modified-id=\"Define-the-model-6.3.1\"><span class=\"toc-item-num\">6.3.1&nbsp;&nbsp;</span>Define the model</a></span></li><li><span><a href=\"#Train-the-model\" data-toc-modified-id=\"Train-the-model-6.3.2\"><span class=\"toc-item-num\">6.3.2&nbsp;&nbsp;</span>Train the model</a></span><ul class=\"toc-item\"><li><span><a href=\"#Validation-error-less-than-training-error?\" data-toc-modified-id=\"Validation-error-less-than-training-error?-6.3.2.1\"><span class=\"toc-item-num\">6.3.2.1&nbsp;&nbsp;</span>Validation error less than training error?</a></span></li><li><span><a href=\"#Why-disable-dropout-during-validation-and-testing?\" data-toc-modified-id=\"Why-disable-dropout-during-validation-and-testing?-6.3.2.2\"><span class=\"toc-item-num\">6.3.2.2&nbsp;&nbsp;</span>Why disable dropout during validation and testing?</a></span></li><li><span><a href=\"#Counting-No.-of-Parameters-in-Deep-Learning-Models-by-Hand\" data-toc-modified-id=\"Counting-No.-of-Parameters-in-Deep-Learning-Models-by-Hand-6.3.2.3\"><span class=\"toc-item-num\">6.3.2.3&nbsp;&nbsp;</span>Counting No. of Parameters in Deep Learning Models by Hand</a></span></li></ul></li><li><span><a href=\"#Plot-validation-performance\" data-toc-modified-id=\"Plot-validation-performance-6.3.3\"><span class=\"toc-item-num\">6.3.3&nbsp;&nbsp;</span>Plot validation performance</a></span></li><li><span><a href=\"#Save-the-model\" data-toc-modified-id=\"Save-the-model-6.3.4\"><span class=\"toc-item-num\">6.3.4&nbsp;&nbsp;</span>Save the model</a></span></li></ul></li><li><span><a href=\"#CNN\" data-toc-modified-id=\"CNN-6.4\"><span class=\"toc-item-num\">6.4&nbsp;&nbsp;</span>CNN</a></span><ul class=\"toc-item\"><li><span><a href=\"#Define-the-model\" data-toc-modified-id=\"Define-the-model-6.4.1\"><span class=\"toc-item-num\">6.4.1&nbsp;&nbsp;</span>Define the model</a></span></li><li><span><a href=\"#Train-the-model\" data-toc-modified-id=\"Train-the-model-6.4.2\"><span class=\"toc-item-num\">6.4.2&nbsp;&nbsp;</span>Train the model</a></span><ul class=\"toc-item\"><li><span><a href=\"#Understanding-and-Calculating-the-number-of-Parameters-in-Convolution-Neural-Networks-(CNNs)\" data-toc-modified-id=\"Understanding-and-Calculating-the-number-of-Parameters-in-Convolution-Neural-Networks-(CNNs)-6.4.2.1\"><span class=\"toc-item-num\">6.4.2.1&nbsp;&nbsp;</span>Understanding and Calculating the number of Parameters in Convolution Neural Networks (CNNs)</a></span></li></ul></li><li><span><a href=\"#Plot-validation-performance\" data-toc-modified-id=\"Plot-validation-performance-6.4.3\"><span class=\"toc-item-num\">6.4.3&nbsp;&nbsp;</span>Plot validation performance</a></span></li><li><span><a href=\"#Save-the-model\" data-toc-modified-id=\"Save-the-model-6.4.4\"><span class=\"toc-item-num\">6.4.4&nbsp;&nbsp;</span>Save the model</a></span></li></ul></li><li><span><a href=\"#CNN-+-GRU\" data-toc-modified-id=\"CNN-+-GRU-6.5\"><span class=\"toc-item-num\">6.5&nbsp;&nbsp;</span>CNN + GRU</a></span><ul class=\"toc-item\"><li><span><a href=\"#Define-the-model\" data-toc-modified-id=\"Define-the-model-6.5.1\"><span class=\"toc-item-num\">6.5.1&nbsp;&nbsp;</span>Define the model</a></span></li><li><span><a href=\"#Train-the-model\" data-toc-modified-id=\"Train-the-model-6.5.2\"><span class=\"toc-item-num\">6.5.2&nbsp;&nbsp;</span>Train the model</a></span><ul class=\"toc-item\"><li><span><a href=\"#Understanding-number-of-params-in-Keras-RNN-and-output-shape-dimension-in-Keras-Embedding-when-RNN-and-Embedding-are-chained-together\" data-toc-modified-id=\"Understanding-number-of-params-in-Keras-RNN-and-output-shape-dimension-in-Keras-Embedding-when-RNN-and-Embedding-are-chained-together-6.5.2.1\"><span class=\"toc-item-num\">6.5.2.1&nbsp;&nbsp;</span>Understanding number of params in Keras RNN and output shape dimension in Keras Embedding when RNN and Embedding are chained together</a></span></li></ul></li><li><span><a href=\"#Plot-validation-performance\" data-toc-modified-id=\"Plot-validation-performance-6.5.3\"><span class=\"toc-item-num\">6.5.3&nbsp;&nbsp;</span>Plot validation performance</a></span></li><li><span><a href=\"#Save-the-model\" data-toc-modified-id=\"Save-the-model-6.5.4\"><span class=\"toc-item-num\">6.5.4&nbsp;&nbsp;</span>Save the model</a></span></li></ul></li><li><span><a href=\"#Transformer-(TODO)\" data-toc-modified-id=\"Transformer-(TODO)-6.6\"><span class=\"toc-item-num\">6.6&nbsp;&nbsp;</span>Transformer (TODO)</a></span></li><li><span><a href=\"#Summary\" data-toc-modified-id=\"Summary-6.7\"><span class=\"toc-item-num\">6.7&nbsp;&nbsp;</span>Summary</a></span></li></ul></li><li><span><a href=\"#Prediction\" data-toc-modified-id=\"Prediction-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>Prediction</a></span><ul class=\"toc-item\"><li><span><a href=\"#Train-the-model-on-the-whole-trainig-set\" data-toc-modified-id=\"Train-the-model-on-the-whole-trainig-set-7.1\"><span class=\"toc-item-num\">7.1&nbsp;&nbsp;</span>Train the model on the whole trainig set</a></span></li><li><span><a href=\"#Increase-the-number-of-epochs\" data-toc-modified-id=\"Increase-the-number-of-epochs-7.2\"><span class=\"toc-item-num\">7.2&nbsp;&nbsp;</span>Increase the number of epochs</a></span></li><li><span><a href=\"#Make-the-prediction-on-the-test-set\" data-toc-modified-id=\"Make-the-prediction-on-the-test-set-7.3\"><span class=\"toc-item-num\">7.3&nbsp;&nbsp;</span>Make the prediction on the test set</a></span></li></ul></li><li><span><a href=\"#Conclusion\" data-toc-modified-id=\"Conclusion-8\"><span class=\"toc-item-num\">8&nbsp;&nbsp;</span>Conclusion</a></span></li><li><span><a href=\"#Future-work\" data-toc-modified-id=\"Future-work-9\"><span class=\"toc-item-num\">9&nbsp;&nbsp;</span>Future work</a></span></li><li><span><a href=\"#Reference\" data-toc-modified-id=\"Reference-10\"><span class=\"toc-item-num\">10&nbsp;&nbsp;</span>Reference</a></span></li></ul></div>"
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    " \n",
    "Which Novel Do I Belong To?\n",
    "\n",
    "In this task, you are expected to learn a Machine Learning model that classifies a given line as belonging to one of the following 12 novels:\n",
    "\n",
    "0: alice_in_wonderland\n",
    "1: dracula\n",
    "2: dubliners\n",
    "3: great_expectations\n",
    "4: hard_times\n",
    "5: huckleberry_finn\n",
    "6: les_miserable\n",
    "7: moby_dick\n",
    "8: oliver_twist\n",
    "9: peter_pan\n",
    "10: talw_of_two_cities\n",
    "11: tom_sawyer\n",
    "\n",
    "\n",
    "Description:\n",
    "\n",
    "You are provided with a zip file (offline_challenge.zip) containing three text files - xtrain.txt, ytrain.txt, xtest.txt. Each line in xtrain.txt and xtest.txt comes from a different novel. The data has been obfuscated, however the patterns in them are preserved. The novel ids corresponding to xtrain.txt are specified in ytrain.txt. You can use these labels to train a Machine Learning model (Deep Learning preferred).\n",
    "\n",
    "With the learned model, predict the novel ids of the lines in xtest.txt (one prediction per line). As part of your submission, include\n",
    "\n",
    "a) your predictions (in the same format as ytrain.txt)\n",
    "\n",
    "b) Expected accuracy on the test set\n",
    "\n",
    "c) the source code for training and prediction (< 10MB)\n",
    "\n",
    "d) a brief description of your method (optional)\n",
    "\n"
   ]
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    "# Put these lines at the top of the notebook to get automatic reloading and inline plotting\n",
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
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   "source": [
    "import string\n",
    "\n",
    "import collections\n",
    "\n",
    "from matplotlib import pyplot\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd \n",
    "\n",
    "import numpy as np \n",
    "from numpy import genfromtxt\n",
    "from numpy.random import seed\n",
    "seed(42) # fix the random seed\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "import keras\n",
    "from keras.models import Model, load_model, Sequential\n",
    "from keras.layers import Dense, Input, Dropout, MaxPooling1D, Conv1D, GlobalMaxPool1D, Bidirectional\n",
    "from keras.layers import LSTM, Lambda, Bidirectional, concatenate, BatchNormalization, Embedding\n",
    "from keras.layers import Reshape, RepeatVector\n",
    "from keras.layers import TimeDistributed\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "from keras.models import load_model\n"
   ]
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    "## Methodology\n",
    "\n",
    "The novel classification task is formulated as a classification problem. \n",
    "Given a sentence x, the object is to predict its label y.\n",
    "To solve this problem, the sentences are transformed into pretrained text representations and then the text representations are fed to various deep learning architectures to classify the sentences into classes. \n",
    "The proposed solution is described as follows.\n",
    "\n",
    "1. Create a character dictionary which contains the mapping of each character in xtrain_obfuscated.txt to an integer.\n",
    " \n",
    " \n",
    "2. Transfer each sentence into a sequence of integers by using the character dictionary created in the first step.\n",
    "\n",
    "\n",
    "3. All the sentences are padded with zeros to make all the sentences have the same length which is the maximum length of the sentences.\n",
    "\n",
    "\n",
    "4. Download the pre-trained word embedding vectors of fastText and create a dictionary which contains the mapping of a character to a vector.\n",
    "\n",
    "\n",
    "5. Create a matrix of one embedding for each character by enumerating all unique character in the character dictionary created in step 1 and locating the embedding weight vector from the loaded embedding from step 4.\n",
    "\n",
    "\n",
    "6. Define a deep learning model. The input layer takes the sequence of integers (generated in step 2) as input.\n",
    "\n",
    "\n",
    "7. The initial weights of the second layer are the values of the embedding matrix which is created in step 5.\n",
    "\n",
    "\n",
    "8. The following layers of the deep learning model are composed of the combination of CNN and GRU layers.\n",
    "\n",
    "\n",
    "9. 80% of the data in xtrain_obfuscated.txt and ytrain.txt is used to train the deep learning model. 20% of the data is considered as the validation set to compute the performance of the trained model.\n",
    "\n",
    "\n",
    "10. Finally, all the data in xtrain_obfuscated.txt and ytrain.txt is used to train the deep learning model. \n",
    "\n",
    "\n",
    "11. The trained model is used to predict the class of the sentences in xtest_obfuscated.txt. \n",
    "\n",
    "\n",
    "12. Save the predictions to ytest.txt.\n",
    "\n"
   ]
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   "source": [
    "## Train, Test, and Validation Sets\n"
   ]
  },
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   "source": [
    "### Load train and test data \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 576,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_file = '../offline_challenge/xtrain_obfuscated.txt'\n",
    "y_train_file = '../offline_challenge/ytrain.txt'\n",
    "x_test_file = '../offline_challenge/xtest_obfuscated.txt'\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 502,
   "metadata": {},
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   "source": [
    "\n",
    "with open(x_train_file) as f:\n",
    "    x_train_all = f.read().strip().lower().split('\\n')\n",
    "    \n",
    "with open(y_train_file) as f:\n",
    "    y_train_all = f.read().strip().split('\\n')\n",
    "y_train_all[:] = [int(y) for y in y_train_all]\n",
    "\n",
    "with open(x_test_file) as f:\n",
    "    x_test = f.read().strip().lower().split('\\n')\n",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32513 32513\n",
      "3000\n"
     ]
    }
   ],
   "source": [
    "print(len(y_train_all), len(y_train_all))\n",
    "print(len(x_test))\n"
   ]
  },
  {
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   "source": [
    "### Split train set into train and validation sets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_valid, y_train, y_valid = train_test_split(x_train_all, y_train_all, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6503 6503\n"
     ]
    }
   ],
   "source": [
    "print(len(x_valid), len(y_valid))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "metadata": {},
   "source": [
    "## Exploratory Data Analysis (EDA)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Class distribution\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
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    {
     "data": {
      "text/plain": [
       "Counter({7: 5097,\n",
       "         3: 4023,\n",
       "         8: 3634,\n",
       "         4: 2337,\n",
       "         1: 3459,\n",
       "         11: 1408,\n",
       "         10: 3052,\n",
       "         9: 980,\n",
       "         6: 4226,\n",
       "         2: 1471,\n",
       "         5: 2283,\n",
       "         0: 543})"
      ]
     },
     "execution_count": 513,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collections.Counter(y_train_all)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
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   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(y_train_all, \n",
    "         bins=12,\n",
    "        )\n",
    "\n",
    "plt.xlabel(\"Novel class\")\n",
    "plt.ylabel(\"Number of train samples\")\n",
    "plt.title(\"Novel class distribution\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It shows that the data is imbalanced. Classes '0' and '10' have very few samples.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sentence length distribution\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sent_len_train = [len(sent) for sent in x_train_all]\n",
    "sent_len_test = [len(sent) for sent in x_test]\n",
    "\n",
    "plt.hist(sent_len_train, \n",
    "         bins=range(min(sent_len_train), max(sent_len_train) + 1, 5),\n",
    "         alpha=0.4,\n",
    "         density=True,\n",
    "         color='green',\n",
    "        )\n",
    "\n",
    "plt.hist(sent_len_test, \n",
    "         bins=range(min(sent_len_test), max(sent_len_test) + 1, 5),\n",
    "         alpha=0.4,\n",
    "         density=True,\n",
    "         color='red',\n",
    "        )\n",
    "\n",
    "labels = ['Train',\"Test\"]\n",
    "plt.legend(labels)\n",
    "plt.xlabel(\"Length of sentence\")\n",
    "plt.ylabel(\"Percentage\")\n",
    "plt.title(\"Sentence length distribution (Train, Test)\")\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot shows the distributions of sentence length in the train set and test set are similar.\n",
    "This shows that the sentences of the train and test might belong to the same distribution.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Character distribution\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 587,
   "metadata": {},
   "outputs": [],
   "source": [
    "character_train_all = [ord(char) for char in sent for sent in x_train_all]\n",
    "character_train_all_counter = collections.Counter(character_train_all)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 588,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13850538"
      ]
     },
     "execution_count": 588,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(character_train_all)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 589,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({117: 1788215,\n",
       "         99: 32513,\n",
       "         121: 97539,\n",
       "         112: 520208,\n",
       "         109: 1137955,\n",
       "         118: 942877,\n",
       "         108: 845338,\n",
       "         101: 1007903,\n",
       "         116: 1040416,\n",
       "         104: 1300520,\n",
       "         115: 292617,\n",
       "         107: 325130,\n",
       "         119: 1072929,\n",
       "         105: 617747,\n",
       "         97: 650260,\n",
       "         100: 260104,\n",
       "         102: 260104,\n",
       "         110: 487695,\n",
       "         120: 162565,\n",
       "         113: 390156,\n",
       "         122: 195078,\n",
       "         103: 260104,\n",
       "         114: 162565})"
      ]
     },
     "execution_count": 589,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "character_train_all_counter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 584,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 584,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(character_train_all_counter)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 590,
   "metadata": {},
   "outputs": [],
   "source": [
    "character_test = [ord(char) for char in sent for sent in x_test]\n",
    "character_test_counter = collections.Counter(character_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 591,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({117: 165000,\n",
       "         99: 3000,\n",
       "         121: 9000,\n",
       "         112: 48000,\n",
       "         109: 105000,\n",
       "         118: 87000,\n",
       "         108: 78000,\n",
       "         101: 93000,\n",
       "         116: 96000,\n",
       "         104: 120000,\n",
       "         115: 27000,\n",
       "         107: 30000,\n",
       "         119: 99000,\n",
       "         105: 57000,\n",
       "         97: 60000,\n",
       "         100: 24000,\n",
       "         102: 24000,\n",
       "         110: 45000,\n",
       "         120: 15000,\n",
       "         113: 36000,\n",
       "         122: 18000,\n",
       "         103: 24000,\n",
       "         114: 15000})"
      ]
     },
     "execution_count": 591,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "character_test_counter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 592,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 592,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(character_test_counter)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 600,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(character_train_all, \n",
    "         bins=range(min(character_train_all), max(character_train_all) + 1, 1),\n",
    "         alpha=0.4,\n",
    "         density=True,\n",
    "         color='green',\n",
    "        )\n",
    "\n",
    "plt.xlabel(\"character index\")\n",
    "plt.ylabel(\"percentage\")\n",
    "plt.title(\"character distribution (train)\")\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 601,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(character_test, \n",
    "         bins=range(min(character_test), max(character_test) + 1, 1),\n",
    "         alpha=0.4,\n",
    "         density=True,\n",
    "         color='red',\n",
    "        )\n",
    "\n",
    "plt.xlabel(\"character index\")\n",
    "plt.ylabel(\"percentage\")\n",
    "plt.title(\"character distribution (test)\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The character distributions of the train and test sets are identical. \n",
    "This shows the text of train and test sets comes from the same distribution.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word Embedding\n",
    "\n",
    "\n",
    "The idea is to transfer each character into a vector based on the context.\n",
    "fastText is used because it is not possible to get words directly from the obfuscated text.\n",
    "\n",
    "\n",
    "**Word Embedding**\n",
    "\n",
    "\"\n",
    "A word embedding is a way of representing text where each word in the vocabulary is represented by a real valued vector in a high-dimensional space. The vectors are learned in such a way that words that have similar meanings will have similar representation in the vector space (close in the vector space). This is a more expressive representation for text than more classical methods like bag-of-words, where relationships between words or tokens are ignored, or forced in bigram and trigram approaches.\n",
    "\n",
    "The real valued vector representation for words can be learned while training the neural network. We can do this in the Keras deep learning library using the Embedding layer.\n",
    "\"\n",
    "\n",
    "\n",
    "https://machinelearningmastery.com/develop-word-embedding-model-predicting-movie-review-sentiment/\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "**What is the difference between fastText and GloVe?**\n",
    "\n",
    "\"\n",
    "Key difference is Glove treats each word in corpus like an atomic entity and generates a vector for each word. \n",
    "In this sense Glove is very much like word2vec- both treat words as the smallest unit to train on.\n",
    "\n",
    "Fasttext which is essentially an extension of word2vec model, treats each word as composed of character ngrams. \n",
    "So the vector for a word is made of the sum of this character n grams. This difference enables fasttext to\n",
    "\n",
    "- Generate better word embeddings for rare words \n",
    "( even if words are rare their character n grams are still shared with other words - hence the embeddings can still be good)\n",
    "\n",
    "- Out of vocabulary words - they can construct the vector for a word from its character n grams even if word doesn't appear in training corpus. Both Glove and word2vec can't.\n",
    "\"\n",
    "\n",
    "https://www.quora.com/What-is-the-difference-between-fastText-and-GloVe\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_embedding(embedding_file):\n",
    "    \"\"\"\n",
    "    Create a dictionary contains key: word/character, value: a vector\n",
    "    \"\"\"\n",
    "    embedding_dict = {}\n",
    "    with open(embedding_file) as f:\n",
    "        for line in tqdm(f):\n",
    "            values = line.split()\n",
    "            word = values[0]\n",
    "            embedding_dict[word] = np.asarray(values[1:], dtype='float32')\n",
    "    return embedding_dict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "400000it [00:35, 11409.46it/s]\n",
      "1999996it [02:52, 11590.40it/s]\n"
     ]
    }
   ],
   "source": [
    "#glove_path = '../glove.6B.300d.txt'\n",
    "#glove_path = '../glove.840B.300d.txt'\n",
    "#glove_embedding = {}\n",
    "#glove_embedding = get_embedding(glove_path)\n",
    "\n",
    "#fasttext_path = '../wiki-news-300d-1M.vec'\n",
    "fasttext_path = '../crawl-300d-2M.vec'\n",
    "fasttext_embedding = {}\n",
    "fasttext_embedding = get_embedding(fasttext_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features (X) and Labels (Y)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tokenization and padding\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "max_features = None\n",
    "token = text.Tokenizer(num_words=max_features, char_level=True)\n",
    "\n",
    "token.fit_on_texts(list(x_train_all) + list(x_test))\n",
    "#token.fit_on_texts(list(x_train_all))\n",
    "\n",
    "# transforms each text in texts to a sequence of integers.\n",
    "x_train_seq = token.texts_to_sequences(x_train)\n",
    "x_train_all_seq = token.texts_to_sequences(x_train_all)\n",
    "x_valid_seq = token.texts_to_sequences(x_valid)\n",
    "x_test_seq = token.texts_to_sequences(x_test)\n",
    "\n",
    "# zero pad the sequences\n",
    "# max_sent_len = 200\n",
    "max_sent_len = max([len(sent) for sent in x_train_all])\n",
    "x_train_seq = sequence.pad_sequences(x_train_seq, maxlen=max_sent_len)\n",
    "x_train_all_seq = sequence.pad_sequences(x_train_all_seq, maxlen=max_sent_len)\n",
    "x_valid_seq = sequence.pad_sequences(x_valid_seq, maxlen=max_sent_len)\n",
    "x_test_seq = sequence.pad_sequences(x_test_seq, maxlen=max_sent_len)\n",
    "\n",
    "word_index = token.word_index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26010\n",
      "32513\n",
      "6503\n",
      "3000\n",
      "452\n",
      "[ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  0  0  0  0  0  0  0  0 15 12  4  9  5 14 12 17  1  2 13 17 15 12  8  7\n",
      "  6  5  8  7 15 12  3  4 13  4  4  9 10  3 21 10  1  2  8  7 11  3 13  4\n",
      "  1  2  9 25 13 17  8  7  4  9 10  3  1  2 10  3  1  6  4  9  3  4  1  2\n",
      "  1  6  8  7  6 16  1  2  9  7  9  7  3  4 19 20  1  2  8  7 11  3 22 21\n",
      " 12 17  1  2  1  2 16 11  1  6 13  4  8  7 12 16 15 18  8  7  6  5  1  2\n",
      "  1  6  5 14  8  7  6  5  6 16  1  2  4  9  3  4  1  2  8  7  6  5  8  7\n",
      " 19 20  1  2 11  3 21 10  3  4 11  3 19 20  1  2 13 17 13  4  8  7 11  3\n",
      " 21 10  5 14 10  3 21 10  1  2  6 16  4  9  3  4  1  6  3  4  5 14 19 20\n",
      "  1  2  8  7 11  3  1  6  1  2 13  4 15 12  6  5  5 14 19 20 10  3  1  6\n",
      "  1  2 13  4  8  7 15 11  8  7  1  2 15 12  8  7  1  6 21 10  5 14  6  5\n",
      "  1  6 13  4  3  4 15 12  1  2  6  5  8  7 11  3  1  2 15 12  8  7  4  9\n",
      "  6  5 12 16  1  6  1  2  1  6  3  4 13  4  6  5  5 14 17 18 22 21  1  2\n",
      " 16 11 13  4  6  5 13 25  1  2 13 17  6 16  4  9  3  4  1  6 11  3  6 16\n",
      "  3  4  9  7  5 14  1  2 22  5 10  3  1  2  1  6  4  9  5 14  1  6 13  4\n",
      "  4  9  3  4  1  2  1  6 13  4  6  5  3  4 22  5  1  2  8  7 11  3  1  6\n",
      "  1  2  4  9  9 17  1  2  1  2 16 11  6  5  8  7  1  6  5 14 19 20  1  2\n",
      "  6  5  8  7 15 12  4  9 12 16  1  2  8  7  6  5  8  7 19 20  1  2 13  4\n",
      "  1  6  8  7  8  7  6  5  1  6 13  4  1  2 15 12  4  9  5 14]\n",
      "{'u': 1, 'h': 2, 'm': 3, 'v': 4, 'e': 5, 'l': 6, 'w': 7, 't': 8, 'i': 9, 'p': 10, 'a': 11, 'k': 12, 'q': 13, 'n': 14, 's': 15, 'r': 16, 'g': 17, 'z': 18, 'd': 19, 'f': 20, 'y': 21, 'x': 22, 'c': 23, 'o': 24, 'j': 25, 'b': 26}\n"
     ]
    }
   ],
   "source": [
    "print(len(x_train_seq))\n",
    "print(len(x_train_all_seq))\n",
    "print(len(x_valid_seq))\n",
    "print(len(x_test_seq))\n",
    "\n",
    "print(len(x_train_seq[0]))\n",
    "print(x_train_seq[0])\n",
    "print(word_index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating an embedding matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_len=300\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {},
   "outputs": [],
   "source": [
    "# integer -> vector\n",
    "def get_embedding_matrix(embedding, \n",
    "                         word_index, \n",
    "                         embedding_len=300):\n",
    "    embedding_matrix = np.zeros((len(word_index) + 1, embedding_len))\n",
    "    for word, i in tqdm(word_index.items()):\n",
    "        embedding_vector = embedding.get(word)\n",
    "        if embedding_vector is not None:\n",
    "            embedding_matrix[i] = embedding_vector\n",
    "    return embedding_matrix\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 26/26 [00:00<00:00, 385.83it/s]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "fasttext_embedding_matrix = get_embedding_matrix(embedding=fasttext_embedding,\n",
    "                                                 word_index=word_index,\n",
    "                                                 embedding_len=embedding_len,\n",
    "                                                )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoding the labels\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_en = np_utils.to_categorical((y_train))\n",
    "y_train_all_en = np_utils.to_categorical((y_train_all))\n",
    "y_valid_en = np_utils.to_categorical((y_valid))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the functions for ploting\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def plot_confusion_matrix(y_test, y_pred):\n",
    "    confusion_mtx = confusion_matrix(y_test, y_pred) \n",
    "    f,ax = plt.subplots(figsize=(8, 8))\n",
    "    sns.heatmap(confusion_mtx, annot=True, linewidths=0.01, cmap=\"Greens\", linecolor=\"gray\", fmt= '.1f',ax=ax)\n",
    "    plt.xlabel(\"Predicted Label\")\n",
    "    plt.ylabel(\"True Label\")\n",
    "    plt.title(\"Confusion Matrix\")\n",
    "    plt.show()\n",
    "    return\n",
    "\n",
    "\n",
    "def plot_learning_curves(history, acc='acc', val_acc='val_acc'):\n",
    "    # plot loss\n",
    "    pyplot.subplot(211)\n",
    "    pyplot.title('Loss')\n",
    "    pyplot.plot(history.history['loss'], color='blue', label='train')\n",
    "    pyplot.plot(history.history['val_loss'], color='orange', label='test')\n",
    "\n",
    "    # plot accuracy\n",
    "    pyplot.subplot(212)\n",
    "    pyplot.title('Accuracy')\n",
    "    pyplot.plot(history.history[acc], color='blue', label='train')\n",
    "    pyplot.plot(history.history[val_acc], color='orange', label='test')\n",
    "    pyplot.legend()\n",
    "    pyplot.show()\n",
    "    \n",
    "    #filename = sys.argv[0].split('/')[-1]\n",
    "    #pyplot.savefig(filename + '_plot.png')\n",
    "    #pyplot.close()\n",
    "    #image = imread(filename + '_plot.png')\n",
    "    #pyplot.figure(figsize=(10, 25))\n",
    "    #pyplot.imshow(image)\n",
    "    return \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_classes = 12\n",
    "batch_size = 32\n",
    "epochs = 60\n",
    "\n",
    "# save all the models in a dictionary\n",
    "model_dict = {}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GRU\n",
    "\n",
    "\n",
    "**Reference**\n",
    "\n",
    "* How to Use Word Embedding Layers for Deep Learning with Keras\n",
    "\n",
    "https://machinelearningmastery.com/use-word-embedding-layers-deep-learning-keras/\n",
    "\n",
    "\n",
    "* A Tour of Recurrent Neural Network Algorithms for Deep Learning\n",
    "\n",
    "https://machinelearningmastery.com/recurrent-neural-network-algorithms-for-deep-learning/\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "metadata": {},
   "outputs": [],
   "source": [
    "def define_gru_model(word_index, \n",
    "                     max_sent_len, \n",
    "                     embedding_matrix,\n",
    "                     embedding_size=0,\n",
    "                     n_classes=12,\n",
    "                    ):\n",
    "    \n",
    "    model = Sequential()\n",
    "    \n",
    "    if embedding_matrix is None:\n",
    "        model.add(Embedding(len(word_index) + 1, \n",
    "                            embedding_size, \n",
    "                            input_length=max_sent_len,\n",
    "                           ))\n",
    "    else:\n",
    "        model.add(Embedding(len(word_index) + 1,\n",
    "                             embedding_matrix.shape[1],\n",
    "                             weights=[embedding_matrix],\n",
    "                             input_length=max_sent_len,\n",
    "                             trainable=False))\n",
    "    \n",
    "    model.add(SpatialDropout1D(0.3))\n",
    "    model.add(GRU(100, dropout=0.3, recurrent_dropout=0.3))\n",
    "    #model.add(GRU(100, dropout=0.3, recurrent_dropout=0.3, return_sequences=True))\n",
    "    #model.add(GRU(100, dropout=0.3, recurrent_dropout=0.3))\n",
    "    #model.add(LSTM(100, dropout=0.3, recurrent_dropout=0.3))\n",
    "\n",
    "    model.add(Dense(64, activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "\n",
    "    model.add(Dense(n_classes))\n",
    "    model.add(Activation('softmax'))\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])\n",
    "\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train the model\n",
    "\n",
    "\n",
    "About early stopping\n",
    "\n",
    "https://medium.com/ai%C2%B3-theory-practice-business/dropout-early-stopping-188a23beb2f9\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/60\n",
      "813/813 [==============================] - 702s 863ms/step - loss: 2.3127 - acc: 0.1758 - val_loss: 2.2221 - val_acc: 0.1927\n",
      "Epoch 2/60\n",
      "813/813 [==============================] - 607s 746ms/step - loss: 2.2314 - acc: 0.2035 - val_loss: 2.2046 - val_acc: 0.2022\n",
      "Epoch 3/60\n",
      "813/813 [==============================] - 583s 717ms/step - loss: 2.2157 - acc: 0.2047 - val_loss: 2.2001 - val_acc: 0.2028\n",
      "Epoch 4/60\n",
      "813/813 [==============================] - 534s 657ms/step - loss: 2.2062 - acc: 0.2080 - val_loss: 2.1963 - val_acc: 0.2041\n",
      "Epoch 5/60\n",
      "813/813 [==============================] - 525s 646ms/step - loss: 2.1559 - acc: 0.2230 - val_loss: 2.0517 - val_acc: 0.2483\n",
      "Epoch 6/60\n",
      "813/813 [==============================] - 589s 724ms/step - loss: 2.0526 - acc: 0.2527 - val_loss: 2.0127 - val_acc: 0.2833\n",
      "Epoch 7/60\n",
      "813/813 [==============================] - 526s 647ms/step - loss: 1.9972 - acc: 0.2832 - val_loss: 1.9324 - val_acc: 0.3119\n",
      "Epoch 8/60\n",
      "813/813 [==============================] - 540s 664ms/step - loss: 1.9391 - acc: 0.3139 - val_loss: 1.8396 - val_acc: 0.3531\n",
      "Epoch 9/60\n",
      "813/813 [==============================] - 561s 690ms/step - loss: 1.8772 - acc: 0.3409 - val_loss: 1.7820 - val_acc: 0.3764\n",
      "Epoch 10/60\n",
      "813/813 [==============================] - 586s 721ms/step - loss: 1.8334 - acc: 0.3611 - val_loss: 1.7400 - val_acc: 0.3924\n",
      "Epoch 11/60\n",
      "813/813 [==============================] - 566s 696ms/step - loss: 1.7790 - acc: 0.3803 - val_loss: 1.6727 - val_acc: 0.4061\n",
      "Epoch 12/60\n",
      "813/813 [==============================] - 518s 637ms/step - loss: 1.6922 - acc: 0.3987 - val_loss: 1.5211 - val_acc: 0.4416\n",
      "Epoch 13/60\n",
      "813/813 [==============================] - 584s 718ms/step - loss: 1.5755 - acc: 0.4306 - val_loss: 1.4457 - val_acc: 0.4687\n",
      "Epoch 14/60\n",
      "813/813 [==============================] - 632s 777ms/step - loss: 1.5296 - acc: 0.4481 - val_loss: 1.4300 - val_acc: 0.4695\n",
      "Epoch 15/60\n",
      "813/813 [==============================] - 642s 790ms/step - loss: 1.4957 - acc: 0.4606 - val_loss: 1.4025 - val_acc: 0.4824\n",
      "Epoch 16/60\n",
      "813/813 [==============================] - 605s 744ms/step - loss: 1.4501 - acc: 0.4750 - val_loss: 1.3368 - val_acc: 0.5191\n",
      "Epoch 17/60\n",
      "813/813 [==============================] - 660s 812ms/step - loss: 1.4160 - acc: 0.4842 - val_loss: 1.3221 - val_acc: 0.5108\n",
      "Epoch 18/60\n",
      "813/813 [==============================] - 598s 736ms/step - loss: 1.3875 - acc: 0.4955 - val_loss: 1.2695 - val_acc: 0.5338\n",
      "Epoch 19/60\n",
      "813/813 [==============================] - 568s 699ms/step - loss: 1.3678 - acc: 0.5044 - val_loss: 1.2641 - val_acc: 0.5374\n",
      "Epoch 20/60\n",
      "813/813 [==============================] - 604s 742ms/step - loss: 1.3361 - acc: 0.5191 - val_loss: 1.2108 - val_acc: 0.5608\n",
      "Epoch 21/60\n",
      "813/813 [==============================] - 585s 719ms/step - loss: 1.3163 - acc: 0.5226 - val_loss: 1.1946 - val_acc: 0.5619\n",
      "Epoch 22/60\n",
      "813/813 [==============================] - 520s 640ms/step - loss: 1.2910 - acc: 0.5360 - val_loss: 1.2016 - val_acc: 0.5642\n",
      "Epoch 23/60\n",
      "813/813 [==============================] - 520s 639ms/step - loss: 1.2857 - acc: 0.5361 - val_loss: 1.1575 - val_acc: 0.5842\n",
      "Epoch 24/60\n",
      "813/813 [==============================] - 566s 696ms/step - loss: 1.2602 - acc: 0.5436 - val_loss: 1.1389 - val_acc: 0.5834\n",
      "Epoch 25/60\n",
      "813/813 [==============================] - 579s 712ms/step - loss: 1.2378 - acc: 0.5577 - val_loss: 1.1491 - val_acc: 0.5830\n",
      "Epoch 26/60\n",
      "813/813 [==============================] - 522s 642ms/step - loss: 1.2332 - acc: 0.5590 - val_loss: 1.1088 - val_acc: 0.5971\n",
      "Epoch 27/60\n",
      "813/813 [==============================] - 535s 658ms/step - loss: 1.2156 - acc: 0.5642 - val_loss: 1.1096 - val_acc: 0.6017\n",
      "Epoch 28/60\n",
      "813/813 [==============================] - 531s 653ms/step - loss: 1.2061 - acc: 0.5702 - val_loss: 1.0882 - val_acc: 0.5994\n",
      "Epoch 29/60\n",
      "813/813 [==============================] - 530s 652ms/step - loss: 1.1935 - acc: 0.5750 - val_loss: 1.0862 - val_acc: 0.6079\n",
      "Epoch 30/60\n",
      "813/813 [==============================] - 530s 652ms/step - loss: 1.1879 - acc: 0.5727 - val_loss: 1.0513 - val_acc: 0.6280\n",
      "Epoch 31/60\n",
      "813/813 [==============================] - 533s 655ms/step - loss: 1.1684 - acc: 0.5830 - val_loss: 1.0597 - val_acc: 0.6174\n",
      "Epoch 32/60\n",
      "813/813 [==============================] - 516s 635ms/step - loss: 1.1608 - acc: 0.5871 - val_loss: 1.0392 - val_acc: 0.6363\n",
      "Epoch 33/60\n",
      "813/813 [==============================] - 506s 622ms/step - loss: 1.1505 - acc: 0.5935 - val_loss: 1.0171 - val_acc: 0.6337\n",
      "Epoch 34/60\n",
      "813/813 [==============================] - 508s 625ms/step - loss: 1.1369 - acc: 0.5959 - val_loss: 1.0122 - val_acc: 0.6457\n",
      "Epoch 35/60\n",
      "813/813 [==============================] - 511s 629ms/step - loss: 1.1268 - acc: 0.6021 - val_loss: 1.0024 - val_acc: 0.6423\n",
      "Epoch 36/60\n",
      "813/813 [==============================] - 504s 619ms/step - loss: 1.1257 - acc: 0.6027 - val_loss: 0.9955 - val_acc: 0.6414\n",
      "Epoch 37/60\n",
      "813/813 [==============================] - 514s 632ms/step - loss: 1.1105 - acc: 0.6126 - val_loss: 1.0142 - val_acc: 0.6399\n",
      "Epoch 38/60\n",
      "813/813 [==============================] - 508s 625ms/step - loss: 1.1062 - acc: 0.6095 - val_loss: 0.9969 - val_acc: 0.6457\n",
      "Epoch 39/60\n",
      "813/813 [==============================] - 512s 630ms/step - loss: 1.0953 - acc: 0.6138 - val_loss: 0.9915 - val_acc: 0.6409\n",
      "Epoch 40/60\n",
      "813/813 [==============================] - 506s 623ms/step - loss: 1.0965 - acc: 0.6116 - val_loss: 0.9981 - val_acc: 0.6405\n",
      "Epoch 41/60\n",
      "813/813 [==============================] - 506s 623ms/step - loss: 1.0875 - acc: 0.6154 - val_loss: 0.9843 - val_acc: 0.6451\n",
      "Epoch 42/60\n",
      "813/813 [==============================] - 512s 630ms/step - loss: 1.0759 - acc: 0.6177 - val_loss: 0.9574 - val_acc: 0.6591\n",
      "Epoch 43/60\n",
      "813/813 [==============================] - 507s 624ms/step - loss: 1.0789 - acc: 0.6200 - val_loss: 0.9645 - val_acc: 0.6514\n",
      "Epoch 44/60\n",
      "813/813 [==============================] - 514s 632ms/step - loss: 1.0739 - acc: 0.6221 - val_loss: 0.9611 - val_acc: 0.6600\n",
      "Epoch 45/60\n",
      "813/813 [==============================] - 505s 622ms/step - loss: 1.0622 - acc: 0.6268 - val_loss: 0.9549 - val_acc: 0.6691\n",
      "Epoch 46/60\n",
      "813/813 [==============================] - 514s 632ms/step - loss: 1.0579 - acc: 0.6250 - val_loss: 0.9453 - val_acc: 0.6606\n",
      "Epoch 47/60\n",
      "813/813 [==============================] - 507s 624ms/step - loss: 1.0557 - acc: 0.6281 - val_loss: 0.9404 - val_acc: 0.6634\n",
      "Epoch 48/60\n",
      "813/813 [==============================] - 508s 625ms/step - loss: 1.0437 - acc: 0.6278 - val_loss: 0.9485 - val_acc: 0.6591\n",
      "Epoch 49/60\n",
      "813/813 [==============================] - 515s 633ms/step - loss: 1.0452 - acc: 0.6332 - val_loss: 0.9409 - val_acc: 0.6631\n",
      "Epoch 50/60\n",
      "813/813 [==============================] - 509s 626ms/step - loss: 1.0299 - acc: 0.6370 - val_loss: 0.9479 - val_acc: 0.6603\n",
      "Epoch 51/60\n",
      "813/813 [==============================] - 510s 628ms/step - loss: 1.0325 - acc: 0.6386 - val_loss: 0.9450 - val_acc: 0.6677\n",
      "Epoch 52/60\n",
      "813/813 [==============================] - 507s 623ms/step - loss: 1.0295 - acc: 0.6378 - val_loss: 0.9361 - val_acc: 0.6686\n",
      "Epoch 53/60\n",
      "813/813 [==============================] - 508s 625ms/step - loss: 1.0322 - acc: 0.6381 - val_loss: 0.9195 - val_acc: 0.6711\n",
      "Epoch 54/60\n",
      "813/813 [==============================] - 511s 629ms/step - loss: 1.0210 - acc: 0.6433 - val_loss: 0.9293 - val_acc: 0.6689\n",
      "Epoch 55/60\n",
      "813/813 [==============================] - 508s 624ms/step - loss: 1.0124 - acc: 0.6447 - val_loss: 0.9182 - val_acc: 0.6765\n",
      "Epoch 56/60\n",
      "813/813 [==============================] - 513s 631ms/step - loss: 1.0078 - acc: 0.6427 - val_loss: 0.9316 - val_acc: 0.6711\n",
      "Epoch 57/60\n",
      "813/813 [==============================] - 511s 628ms/step - loss: 1.0081 - acc: 0.6446 - val_loss: 0.9021 - val_acc: 0.6792\n",
      "Epoch 58/60\n",
      "813/813 [==============================] - 509s 626ms/step - loss: 1.0053 - acc: 0.6444 - val_loss: 0.9111 - val_acc: 0.6765\n",
      "Epoch 59/60\n",
      "813/813 [==============================] - 505s 622ms/step - loss: 1.0028 - acc: 0.6452 - val_loss: 0.8922 - val_acc: 0.6868\n",
      "Epoch 60/60\n",
      "813/813 [==============================] - 513s 631ms/step - loss: 0.9981 - acc: 0.6519 - val_loss: 0.9087 - val_acc: 0.6834\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model = define_gru_model(word_index=word_index, \n",
    "                         max_sent_len=max_sent_len, \n",
    "                         embedding_matrix=fasttext_embedding_matrix,\n",
    "                        )\n",
    "\n",
    "#earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=3, verbose=0, mode='auto')\n",
    "\n",
    "history = model.fit(x=x_train_seq, \n",
    "                    y=y_train_en, \n",
    "                    batch_size=batch_size, \n",
    "                    epochs=epochs, \n",
    "                    verbose=1, \n",
    "                    validation_data=(x_valid_seq, y_valid_en),\n",
    "                    #callbacks=[earlystop],\n",
    "                   )\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curves(history)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Validation error less than training error?\n",
    "\n",
    "https://stats.stackexchange.com/questions/187335/validation-error-less-than-training-error\n",
    "\n",
    "\n",
    "\"\n",
    "Generally speaking though, training error will almost always underestimate your validation error. However it is possible for the validation error to be less than the training. You can think of it two ways:\n",
    "\n",
    "- Your training set had many 'hard' cases to learn\n",
    "- Your validation set had mostly 'easy' cases to predict\n",
    "\"\n",
    "\n",
    "\"\n",
    "One possibility: If you are using dropout regularization layer in your network, it is reasonable that the validation error is smaller than training error. Because usually dropout is activated when training but deactivated when evaluating on the validation set. You get a more smooth (usually means better) function in the latter case.\n",
    "\"\n",
    "\n",
    "\"\n",
    "FAQ on Keras' documentation:\n",
    "\n",
    "Why is the training loss much higher than the testing loss?\n",
    "\n",
    "A Keras model has two modes: training and testing. Regularization mechanisms, such as Dropout and L1/L2 weight regularization, are turned off at testing time.\n",
    "\n",
    "Besides, the training loss is the average of the losses over each batch of training data. Because your model is changing over time, the loss over the first batches of an epoch is generally higher than over the last batches. On the other hand, the testing loss for an epoch is computed using the model as it is at the end of the epoch, resulting in a lower loss.\n",
    "\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Why disable dropout during validation and testing?\n",
    "\n",
    "https://stackoverflow.com/questions/44223585/why-disable-dropout-during-validation-and-testing/44229870#:~:text=Dropout%20is%20a%20random%20process,a%20layer%20with%20chance%20p.&text=However%2C%20there%20are%20two%20main,every%20(sequences%20of)%20activation.\n",
    "\n",
    "\"\n",
    "Dropout is a random process of disabling neurons in a layer with chance p. This will make certain neurons feel they are 'wrong' in each iteration - basically, you are making neurons feel 'wrong' about their output so that they rely less on the outputs of the nodes in the previous layer. This is a method of regularization and reduces overfitting.\n",
    "\n",
    "However, there are two main reasons you should not use dropout to test data:\n",
    "\n",
    "- Dropout makes neurons output 'wrong' values on purpose\n",
    "\n",
    "- Because you disable neurons randomly, your network will have different outputs every (sequences of) activation. \n",
    "This undermines consistency.\n",
    "\"\n",
    "\n",
    "\n",
    "##### Counting No. of Parameters in Deep Learning Models by Hand\n",
    "\n",
    "https://towardsdatascience.com/counting-no-of-parameters-in-deep-learning-models-by-hand-8f1716241889\n",
    "\n",
    "https://stackoverflow.com/questions/50134334/number-of-parameters-for-keras-simplernn\n",
    "\n",
    "- RNNs\n",
    "\n",
    " recurrent_weights + input_weights + biases\n",
    "\n",
    "*resp: (num_features + num_units)* num_units + num_units\n",
    "\n",
    "num_units = equals the number of units in the RNN\n",
    "\n",
    "num_features = equals the number features of your input\n",
    "\n",
    "recurrent_weights = num_units*num_units\n",
    "\n",
    "input_weights = num_features*num_units\n",
    "\n",
    "biases = num_units*1\n",
    "\n",
    "num_units* num_units + num_features* num_units + biases\n",
    "\n",
    "= (num_features + num_units)* num_units + biases\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dict['gru_fasttext'] = model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(y_valid, \n",
    "         bins=12,\n",
    "        )\n",
    "\n",
    "plt.xlabel(\"Novel class\")\n",
    "plt.ylabel(\"Number of validation samples\")\n",
    "plt.title(\"Novel class distribution\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot validation performance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_val = model.predict_classes(x_valid_seq)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_valid, y_pred_val)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.70      0.76        98\n",
      "           1       0.78      0.55      0.65       678\n",
      "           2       0.85      0.45      0.59       282\n",
      "           3       0.57      0.79      0.66       819\n",
      "           4       0.89      0.68      0.77       460\n",
      "           5       0.94      0.80      0.86       469\n",
      "           6       0.64      0.72      0.68       841\n",
      "           7       0.72      0.81      0.76       965\n",
      "           8       0.78      0.84      0.81       742\n",
      "           9       0.89      0.17      0.29       187\n",
      "          10       0.41      0.51      0.46       658\n",
      "          11       0.71      0.50      0.59       304\n",
      "\n",
      "    accuracy                           0.68      6503\n",
      "   macro avg       0.75      0.63      0.66      6503\n",
      "weighted avg       0.71      0.68      0.68      6503\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_valid, y_pred_val))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dict['gru_fasttext'].save('gru_fasttext_{}.h5'.format(epochs))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN\n",
    "\n",
    "\n",
    "**Reference**\n",
    "\n",
    "* How to Develop a Multichannel CNN Model for Text Classification\n",
    "\n",
    "\n",
    "https://machinelearningmastery.com/develop-n-gram-multichannel-convolutional-neural-network-sentiment-analysis/\n",
    "\n",
    "https://www.kaggle.com/kmader/character-level-cnn-classification-with-dilations\n",
    "\n",
    "https://machinelearningmastery.com/best-practices-document-classification-deep-learning/\n",
    "\n"
   ]
  },
  {
   "attachments": {
    "Depiction-of-the-multiple-channel-convolutional-neural-network-for-text.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Depiction-of-the-multiple-channel-convolutional-neural-network-for-text.png](attachment:Depiction-of-the-multiple-channel-convolutional-neural-network-for-text.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Depiction of the multiple-channel convolutional neural network for text.\n",
    "Taken from “Convolutional Neural Networks for Sentence Classification.”\n",
    "\n",
    "\n",
    "\n",
    "\"\n",
    "A standard model for document classification is to use an Embedding layer as input, followed by a one-dimensional convolutional neural network, pooling layer, and then a prediction output layer.\n",
    "\n",
    "The kernel size in the convolutional layer defines the number of words to consider as the convolution is passed across the input text document, providing a grouping parameter.\n",
    "\"\n",
    "\n",
    "https://machinelearningmastery.com/develop-n-gram-multichannel-convolutional-neural-network-sentiment-analysis/\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {},
   "outputs": [],
   "source": [
    "def define_cnn_model(word_index, \n",
    "                     max_sent_len,\n",
    "                     embedding_matrix,\n",
    "                     embedding_size=0,\n",
    "                     n_classes=12,\n",
    "                    ):\n",
    "    model = Sequential()\n",
    "    \n",
    "    if embedding_matrix is None:\n",
    "        model.add(Embedding(len(word_index) + 1, \n",
    "                            embedding_size, \n",
    "                            input_length=max_sent_len,\n",
    "                           ))\n",
    "    else:\n",
    "        model.add(Embedding(len(word_index) + 1,\n",
    "                             embedding_matrix.shape[1],\n",
    "                             weights=[embedding_matrix],\n",
    "                             input_length=max_sent_len,\n",
    "                             trainable=False))\n",
    "    model.add(Dropout(0.3))\n",
    "\n",
    "    # add a Convolution1D, which will learn filters\n",
    "    # word group filters of size filter_length:\n",
    "    model.add(Conv1D(64,\n",
    "                     5,\n",
    "                     activation='relu'))\n",
    "    model.add(MaxPooling1D(5))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Conv1D(128,\n",
    "                     5,\n",
    "                     activation='relu'))\n",
    "    model.add(MaxPooling1D(5))\n",
    "    model.add(BatchNormalization())\n",
    "    #model.add(Conv1D(256,\n",
    "    #                 5,\n",
    "    #                 activation='relu'))\n",
    "    #model.add(BatchNormalization())\n",
    "    #model.add(MaxPooling1D(3))\n",
    "    #model.add(BatchNormalization())\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(64, activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "\n",
    "    model.add(Dense(n_classes, activation='softmax'))\n",
    "\n",
    "    model.compile(loss='categorical_crossentropy',\n",
    "                  optimizer='adam',\n",
    "                  metrics=['acc'])\n",
    "    \n",
    "    return model\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* TODO: \n",
    "\n",
    "multi-channel convolutional neural network\n",
    "\n",
    "https://machinelearningmastery.com/develop-n-gram-multichannel-convolutional-neural-network-sentiment-analysis/\n",
    "\n",
    "\"\n",
    "A multi-channel convolutional neural network for document classification involves using multiple versions of the standard model with different sized kernels. This allows the document to be processed at different resolutions or different n-grams (groups of words) at a time, whilst the model learns how to best integrate these interpretations.\n",
    "\"\n",
    "\n",
    "def define_model(length, vocab_size):\n",
    "\t# channel 1\n",
    "\tinputs1 = Input(shape=(length,))\n",
    "\tembedding1 = Embedding(vocab_size, 100)(inputs1)\n",
    "\tconv1 = Conv1D(filters=32, kernel_size=4, activation='relu')(embedding1)\n",
    "\tdrop1 = Dropout(0.5)(conv1)\n",
    "\tpool1 = MaxPooling1D(pool_size=2)(drop1)\n",
    "\tflat1 = Flatten()(pool1)\n",
    "\t# channel 2\n",
    "\tinputs2 = Input(shape=(length,))\n",
    "\tembedding2 = Embedding(vocab_size, 100)(inputs2)\n",
    "\tconv2 = Conv1D(filters=32, kernel_size=6, activation='relu')(embedding2)\n",
    "\tdrop2 = Dropout(0.5)(conv2)\n",
    "\tpool2 = MaxPooling1D(pool_size=2)(drop2)\n",
    "\tflat2 = Flatten()(pool2)\n",
    "\t# channel 3\n",
    "\tinputs3 = Input(shape=(length,))\n",
    "\tembedding3 = Embedding(vocab_size, 100)(inputs3)\n",
    "\tconv3 = Conv1D(filters=32, kernel_size=8, activation='relu')(embedding3)\n",
    "\tdrop3 = Dropout(0.5)(conv3)\n",
    "\tpool3 = MaxPooling1D(pool_size=2)(drop3)\n",
    "\tflat3 = Flatten()(pool3)\n",
    "\t# merge\n",
    "\tmerged = concatenate([flat1, flat2, flat3])\n",
    "\t# interpretation\n",
    "\tdense1 = Dense(10, activation='relu')(merged)\n",
    "\toutputs = Dense(1, activation='sigmoid')(dense1)\n",
    "\tmodel = Model(inputs=[inputs1, inputs2, inputs3], outputs=outputs)\n",
    "\t# compile\n",
    "\tmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\t# summarize\n",
    "\tprint(model.summary())\n",
    "\tplot_model(model, show_shapes=True, to_file='multichannel.png')\n",
    "\treturn model\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/60\n",
      "813/813 [==============================] - 97s 120ms/step - loss: 2.3381 - acc: 0.1732 - val_loss: 2.2006 - val_acc: 0.1916\n",
      "Epoch 2/60\n",
      "813/813 [==============================] - 101s 124ms/step - loss: 2.1300 - acc: 0.2396 - val_loss: 2.3044 - val_acc: 0.2288\n",
      "Epoch 3/60\n",
      "813/813 [==============================] - 85s 104ms/step - loss: 1.9358 - acc: 0.3209 - val_loss: 1.7205 - val_acc: 0.3961\n",
      "Epoch 4/60\n",
      "813/813 [==============================] - 76s 93ms/step - loss: 1.7608 - acc: 0.3875 - val_loss: 1.8351 - val_acc: 0.3794\n",
      "Epoch 5/60\n",
      "813/813 [==============================] - 71s 88ms/step - loss: 1.6070 - acc: 0.4396 - val_loss: 3.4314 - val_acc: 0.3109\n",
      "Epoch 6/60\n",
      "813/813 [==============================] - 70s 86ms/step - loss: 1.5019 - acc: 0.4745 - val_loss: 1.7424 - val_acc: 0.4237\n",
      "Epoch 7/60\n",
      "813/813 [==============================] - 70s 86ms/step - loss: 1.4224 - acc: 0.5026 - val_loss: 1.8095 - val_acc: 0.4283\n",
      "Epoch 8/60\n",
      "813/813 [==============================] - 69s 85ms/step - loss: 1.3354 - acc: 0.5346 - val_loss: 1.6642 - val_acc: 0.4338\n",
      "Epoch 9/60\n",
      "813/813 [==============================] - 69s 85ms/step - loss: 1.2620 - acc: 0.5628 - val_loss: 1.4286 - val_acc: 0.5061\n",
      "Epoch 10/60\n",
      "813/813 [==============================] - 71s 88ms/step - loss: 1.2026 - acc: 0.5792 - val_loss: 1.4534 - val_acc: 0.5256\n",
      "Epoch 11/60\n",
      "813/813 [==============================] - 73s 90ms/step - loss: 1.1262 - acc: 0.6070 - val_loss: 1.5149 - val_acc: 0.5135\n",
      "Epoch 12/60\n",
      "813/813 [==============================] - 98s 121ms/step - loss: 1.0658 - acc: 0.6278 - val_loss: 1.3155 - val_acc: 0.5544\n",
      "Epoch 13/60\n",
      "813/813 [==============================] - 100s 124ms/step - loss: 1.0153 - acc: 0.6421 - val_loss: 1.3965 - val_acc: 0.5462\n",
      "Epoch 14/60\n",
      "813/813 [==============================] - 94s 115ms/step - loss: 0.9653 - acc: 0.6607 - val_loss: 1.4420 - val_acc: 0.5456\n",
      "Epoch 15/60\n",
      "813/813 [==============================] - 86s 105ms/step - loss: 0.9329 - acc: 0.6733 - val_loss: 1.3847 - val_acc: 0.5711\n",
      "Epoch 16/60\n",
      "813/813 [==============================] - 87s 107ms/step - loss: 0.8898 - acc: 0.6890 - val_loss: 1.3376 - val_acc: 0.5885\n",
      "Epoch 17/60\n",
      "813/813 [==============================] - 81s 99ms/step - loss: 0.8509 - acc: 0.6989 - val_loss: 1.3377 - val_acc: 0.5971\n",
      "Epoch 18/60\n",
      "813/813 [==============================] - 90s 110ms/step - loss: 0.8090 - acc: 0.7168 - val_loss: 1.3607 - val_acc: 0.5819\n",
      "Epoch 19/60\n",
      "813/813 [==============================] - 81s 100ms/step - loss: 0.7790 - acc: 0.7255 - val_loss: 1.4903 - val_acc: 0.5799\n",
      "Epoch 20/60\n",
      "813/813 [==============================] - 88s 108ms/step - loss: 0.7598 - acc: 0.7277 - val_loss: 1.4960 - val_acc: 0.5842\n",
      "Epoch 21/60\n",
      "813/813 [==============================] - 88s 108ms/step - loss: 0.7297 - acc: 0.7411 - val_loss: 1.3015 - val_acc: 0.6102\n",
      "Epoch 22/60\n",
      "813/813 [==============================] - 86s 106ms/step - loss: 0.7029 - acc: 0.7530 - val_loss: 1.4300 - val_acc: 0.5863\n",
      "Epoch 23/60\n",
      "813/813 [==============================] - 89s 109ms/step - loss: 0.6858 - acc: 0.7584 - val_loss: 1.4942 - val_acc: 0.5850\n",
      "Epoch 24/60\n",
      "813/813 [==============================] - 108s 133ms/step - loss: 0.6592 - acc: 0.7663 - val_loss: 1.7847 - val_acc: 0.5556\n",
      "Epoch 25/60\n",
      "813/813 [==============================] - 98s 120ms/step - loss: 0.6438 - acc: 0.7727 - val_loss: 1.3172 - val_acc: 0.6073\n",
      "Epoch 26/60\n",
      "813/813 [==============================] - 84s 104ms/step - loss: 0.6330 - acc: 0.7795 - val_loss: 1.6692 - val_acc: 0.5842\n",
      "Epoch 27/60\n",
      "813/813 [==============================] - 86s 106ms/step - loss: 0.6062 - acc: 0.7845 - val_loss: 1.5259 - val_acc: 0.5925\n",
      "Epoch 28/60\n",
      "813/813 [==============================] - 86s 106ms/step - loss: 0.5928 - acc: 0.7870 - val_loss: 2.2724 - val_acc: 0.5468\n",
      "Epoch 29/60\n",
      "813/813 [==============================] - 91s 112ms/step - loss: 0.5808 - acc: 0.7953 - val_loss: 1.5965 - val_acc: 0.5825\n",
      "Epoch 30/60\n",
      "813/813 [==============================] - 87s 107ms/step - loss: 0.5560 - acc: 0.8022 - val_loss: 1.6511 - val_acc: 0.5942\n",
      "Epoch 31/60\n",
      "813/813 [==============================] - 84s 103ms/step - loss: 0.5573 - acc: 0.8027 - val_loss: 1.7164 - val_acc: 0.5983\n",
      "Epoch 32/60\n",
      "813/813 [==============================] - 79s 98ms/step - loss: 0.5386 - acc: 0.8080 - val_loss: 1.8590 - val_acc: 0.5899\n",
      "Epoch 33/60\n",
      "813/813 [==============================] - 82s 101ms/step - loss: 0.5316 - acc: 0.8136 - val_loss: 1.6102 - val_acc: 0.6071\n",
      "Epoch 34/60\n",
      "813/813 [==============================] - 99s 122ms/step - loss: 0.5223 - acc: 0.8162 - val_loss: 1.5101 - val_acc: 0.6070\n",
      "Epoch 35/60\n",
      "813/813 [==============================] - 87s 107ms/step - loss: 0.5122 - acc: 0.8176 - val_loss: 1.8599 - val_acc: 0.5970\n",
      "Epoch 36/60\n",
      "813/813 [==============================] - 88s 109ms/step - loss: 0.4945 - acc: 0.8246 - val_loss: 2.0120 - val_acc: 0.5916\n",
      "Epoch 37/60\n",
      "813/813 [==============================] - 86s 106ms/step - loss: 0.5011 - acc: 0.8232 - val_loss: 2.0292 - val_acc: 0.5879\n",
      "Epoch 38/60\n",
      "813/813 [==============================] - 85s 105ms/step - loss: 0.4845 - acc: 0.8274 - val_loss: 2.0253 - val_acc: 0.5768\n",
      "Epoch 39/60\n",
      "813/813 [==============================] - 81s 99ms/step - loss: 0.4726 - acc: 0.8337 - val_loss: 1.6576 - val_acc: 0.6042\n",
      "Epoch 40/60\n",
      "813/813 [==============================] - 79s 98ms/step - loss: 0.4765 - acc: 0.8312 - val_loss: 1.9674 - val_acc: 0.5968\n",
      "Epoch 41/60\n",
      "813/813 [==============================] - 86s 106ms/step - loss: 0.4700 - acc: 0.8335 - val_loss: 1.8117 - val_acc: 0.5977\n",
      "Epoch 42/60\n",
      "813/813 [==============================] - 88s 109ms/step - loss: 0.4462 - acc: 0.8419 - val_loss: 1.6892 - val_acc: 0.6103\n",
      "Epoch 43/60\n",
      "813/813 [==============================] - 84s 104ms/step - loss: 0.4491 - acc: 0.8428 - val_loss: 2.0340 - val_acc: 0.5541\n",
      "Epoch 44/60\n",
      "813/813 [==============================] - 79s 98ms/step - loss: 0.4502 - acc: 0.8420 - val_loss: 1.5950 - val_acc: 0.6186\n",
      "Epoch 45/60\n",
      "813/813 [==============================] - 70s 86ms/step - loss: 0.4420 - acc: 0.8468 - val_loss: 1.7125 - val_acc: 0.6093\n",
      "Epoch 46/60\n",
      "813/813 [==============================] - 71s 87ms/step - loss: 0.4270 - acc: 0.8501 - val_loss: 1.7317 - val_acc: 0.6140\n",
      "Epoch 47/60\n",
      "813/813 [==============================] - 70s 86ms/step - loss: 0.4100 - acc: 0.8522 - val_loss: 1.8543 - val_acc: 0.6076\n",
      "Epoch 48/60\n",
      "813/813 [==============================] - 81s 99ms/step - loss: 0.4174 - acc: 0.8542 - val_loss: 1.7441 - val_acc: 0.6099\n",
      "Epoch 49/60\n",
      "813/813 [==============================] - 73s 90ms/step - loss: 0.3963 - acc: 0.8577 - val_loss: 1.8299 - val_acc: 0.6185\n",
      "Epoch 50/60\n",
      "813/813 [==============================] - 69s 85ms/step - loss: 0.4037 - acc: 0.8578 - val_loss: 2.2898 - val_acc: 0.5796\n",
      "Epoch 51/60\n",
      "813/813 [==============================] - 72s 89ms/step - loss: 0.3929 - acc: 0.8624 - val_loss: 1.7935 - val_acc: 0.6102\n",
      "Epoch 52/60\n",
      "813/813 [==============================] - 76s 93ms/step - loss: 0.3833 - acc: 0.8649 - val_loss: 1.8561 - val_acc: 0.6054\n",
      "Epoch 53/60\n",
      "813/813 [==============================] - 108s 133ms/step - loss: 0.3895 - acc: 0.8630 - val_loss: 2.0772 - val_acc: 0.6037\n",
      "Epoch 54/60\n",
      "813/813 [==============================] - 92s 113ms/step - loss: 0.3872 - acc: 0.8651 - val_loss: 2.1866 - val_acc: 0.5936\n",
      "Epoch 55/60\n",
      "813/813 [==============================] - 87s 106ms/step - loss: 0.3916 - acc: 0.8629 - val_loss: 1.8708 - val_acc: 0.6143\n",
      "Epoch 56/60\n",
      "813/813 [==============================] - 78s 95ms/step - loss: 0.3804 - acc: 0.8662 - val_loss: 1.8660 - val_acc: 0.6165\n",
      "Epoch 57/60\n",
      "813/813 [==============================] - 85s 105ms/step - loss: 0.3560 - acc: 0.8751 - val_loss: 1.8372 - val_acc: 0.6251\n",
      "Epoch 58/60\n",
      "813/813 [==============================] - 101s 125ms/step - loss: 0.3702 - acc: 0.8721 - val_loss: 1.7965 - val_acc: 0.6133\n",
      "Epoch 59/60\n",
      "813/813 [==============================] - 126s 155ms/step - loss: 0.3757 - acc: 0.8693 - val_loss: 1.8793 - val_acc: 0.6114\n",
      "Epoch 60/60\n",
      "813/813 [==============================] - 127s 156ms/step - loss: 0.3556 - acc: 0.8766 - val_loss: 1.8658 - val_acc: 0.6136\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model = define_cnn_model(word_index=word_index, \n",
    "                         max_sent_len=max_sent_len,\n",
    "                         embedding_matrix=fasttext_embedding_matrix,\n",
    "                        )\n",
    "\n",
    "#earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=3, verbose=0, mode='auto')\n",
    "history = model.fit(x=x_train_seq, \n",
    "                    y=y_train_en, \n",
    "                    batch_size=batch_size, \n",
    "                    epochs=epochs, \n",
    "                    verbose=1, \n",
    "                    validation_data=(x_valid_seq, y_valid_en),\n",
    "                    #callbacks=[earlystop],\n",
    "                   )\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_52\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_60 (Embedding)     (None, 452, 300)          8100      \n",
      "_________________________________________________________________\n",
      "dropout_69 (Dropout)         (None, 452, 300)          0         \n",
      "_________________________________________________________________\n",
      "conv1d_45 (Conv1D)           (None, 448, 64)           96064     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_45 (MaxPooling (None, 89, 64)            0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_31 (Batc (None, 89, 64)            256       \n",
      "_________________________________________________________________\n",
      "conv1d_46 (Conv1D)           (None, 85, 128)           41088     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_46 (MaxPooling (None, 17, 128)           0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_32 (Batc (None, 17, 128)           512       \n",
      "_________________________________________________________________\n",
      "flatten_13 (Flatten)         (None, 2176)              0         \n",
      "_________________________________________________________________\n",
      "dense_122 (Dense)            (None, 64)                139328    \n",
      "_________________________________________________________________\n",
      "dropout_70 (Dropout)         (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_123 (Dense)            (None, 12)                780       \n",
      "=================================================================\n",
      "Total params: 286,128\n",
      "Trainable params: 277,644\n",
      "Non-trainable params: 8,484\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Understanding and Calculating the number of Parameters in Convolution Neural Networks (CNNs)\n",
    "\n",
    "https://towardsdatascience.com/understanding-and-calculating-the-number-of-parameters-in-convolution-neural-networks-cnns-fc88790d530d\n",
    "\n",
    "https://stackoverflow.com/questions/42786717/how-to-calculate-the-number-of-parameters-for-convolutional-neural-network\n",
    "\n",
    "\n",
    "- **Input layer**\n",
    "\n",
    "So no learnable parameters here. Thus number of parameters = 0.\n",
    "\n",
    "- **CONV layer**\n",
    "\n",
    "All we have to do is just multiply the by the shape of width m, height n, previous layer’s filters d and account for all such filters k in the current layer. Don’t forget the bias term for each of the filter. \n",
    "Number of parameters in a CONV layer would be : \n",
    "\n",
    "((m * n * d)+1)* k), \n",
    "\n",
    "\n",
    "- **POOL layer**\n",
    "\n",
    "The number of parameters = 0.\n",
    "\n",
    "\n",
    "- **Fully Connected Layer (FC)**\n",
    "\n",
    "The product of the number of neurons in the current layer c and the number of neurons on the previous layer p and as always, do not forget the bias term. \n",
    "Thus number of parameters here are: \n",
    "((current layer neurons c * previous layer neurons p)+1*c).\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curves(history)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dict['cnn_fasttext'] = model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot validation performance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_val = model.predict_classes(x_valid_seq)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_valid, y_pred_val)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.66      0.56      0.61        98\n",
      "           1       0.56      0.62      0.59       678\n",
      "           2       0.39      0.55      0.45       282\n",
      "           3       0.61      0.59      0.60       819\n",
      "           4       0.77      0.64      0.70       460\n",
      "           5       0.76      0.90      0.82       469\n",
      "           6       0.59      0.67      0.63       841\n",
      "           7       0.72      0.69      0.71       965\n",
      "           8       0.84      0.69      0.76       742\n",
      "           9       0.27      0.36      0.31       187\n",
      "          10       0.46      0.37      0.41       658\n",
      "          11       0.37      0.35      0.36       304\n",
      "\n",
      "    accuracy                           0.61      6503\n",
      "   macro avg       0.58      0.58      0.58      6503\n",
      "weighted avg       0.62      0.61      0.61      6503\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_valid, y_pred_val))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dict['cnn_fasttext'].save('cnn_fasttext_{}.h5'.format(epochs))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN + GRU\n",
    "\n",
    "\n",
    "**Reference**\n",
    "\n",
    "* Tackling Toxic Problem with Char-Gram+CNN+LSTM\n",
    "\n",
    "https://www.kaggle.com/sbongo/tackling-toxic-problem-with-char-gram-cnn-lstm\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "metadata": {},
   "outputs": [],
   "source": [
    "def define_cnn_gru_model(word_index, \n",
    "                         max_sent_len,\n",
    "                         embedding_matrix,\n",
    "                         embedding_size=0,\n",
    "                         n_classes=12,\n",
    "                         ):\n",
    "    \n",
    "    inp = Input(shape=(max_sent_len, ))\n",
    "    \n",
    "    if embedding_matrix is None:\n",
    "        x = Embedding(len(word_index)+1, embedding_size)(inp)\n",
    "        \n",
    "    else:\n",
    "        x = Embedding(len(word_index) + 1,\n",
    "                      embedding_matrix.shape[1],\n",
    "                      weights=[embedding_matrix],\n",
    "                      input_length=max_sent_len,\n",
    "                      trainable=False)(inp)\n",
    "        \n",
    "    x = Conv1D(filters=100, kernel_size=4, padding='same', activation='relu')(x)\n",
    "    x = MaxPooling1D(pool_size=4)(x)\n",
    "    x = Bidirectional(GRU(60, return_sequences=True, name='lstm_layer', dropout=0.2, recurrent_dropout=0.2))(x)\n",
    "    x = GlobalMaxPool1D()(x)\n",
    "    x = Dense(50, activation=\"relu\")(x)\n",
    "    x = Dropout(0.2)(x)\n",
    "    x = Dense(n_classes, activation=\"sigmoid\")(x)\n",
    "    \n",
    "    model = Model(inputs=inp, outputs=x)\n",
    "    model.compile(loss='categorical_crossentropy',\n",
    "                  optimizer='adam',\n",
    "                  metrics=['accuracy'],\n",
    "                 )\n",
    "    \n",
    "    return model\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/60\n",
      "813/813 [==============================] - 197s 242ms/step - loss: 2.3428 - accuracy: 0.1575 - val_loss: 2.3021 - val_accuracy: 0.1608\n",
      "Epoch 2/60\n",
      "813/813 [==============================] - 179s 221ms/step - loss: 1.9772 - accuracy: 0.2835 - val_loss: 1.5645 - val_accuracy: 0.4070\n",
      "Epoch 3/60\n",
      "813/813 [==============================] - 180s 222ms/step - loss: 1.5350 - accuracy: 0.4333 - val_loss: 1.4064 - val_accuracy: 0.4833\n",
      "Epoch 4/60\n",
      "813/813 [==============================] - 181s 222ms/step - loss: 1.4334 - accuracy: 0.4849 - val_loss: 1.3327 - val_accuracy: 0.5142\n",
      "Epoch 5/60\n",
      "813/813 [==============================] - 178s 219ms/step - loss: 1.3506 - accuracy: 0.5124 - val_loss: 1.2525 - val_accuracy: 0.5436\n",
      "Epoch 6/60\n",
      "813/813 [==============================] - 179s 221ms/step - loss: 1.2734 - accuracy: 0.5439 - val_loss: 1.2027 - val_accuracy: 0.5634\n",
      "Epoch 7/60\n",
      "813/813 [==============================] - 181s 223ms/step - loss: 1.2132 - accuracy: 0.5669 - val_loss: 1.1293 - val_accuracy: 0.5919\n",
      "Epoch 8/60\n",
      "813/813 [==============================] - 180s 222ms/step - loss: 1.1575 - accuracy: 0.5902 - val_loss: 1.0749 - val_accuracy: 0.6096\n",
      "Epoch 9/60\n",
      "813/813 [==============================] - 182s 224ms/step - loss: 1.1080 - accuracy: 0.6072 - val_loss: 1.0315 - val_accuracy: 0.6240\n",
      "Epoch 10/60\n",
      "813/813 [==============================] - 180s 221ms/step - loss: 1.0631 - accuracy: 0.6248 - val_loss: 0.9899 - val_accuracy: 0.6465\n",
      "Epoch 11/60\n",
      "813/813 [==============================] - 180s 221ms/step - loss: 1.0145 - accuracy: 0.6378 - val_loss: 0.9952 - val_accuracy: 0.6466\n",
      "Epoch 12/60\n",
      "813/813 [==============================] - 181s 222ms/step - loss: 0.9903 - accuracy: 0.6501 - val_loss: 0.9129 - val_accuracy: 0.6662\n",
      "Epoch 13/60\n",
      "813/813 [==============================] - 180s 221ms/step - loss: 0.9602 - accuracy: 0.6590 - val_loss: 0.8988 - val_accuracy: 0.6722\n",
      "Epoch 14/60\n",
      "813/813 [==============================] - 181s 222ms/step - loss: 0.9353 - accuracy: 0.6705 - val_loss: 0.8687 - val_accuracy: 0.6841\n",
      "Epoch 15/60\n",
      "813/813 [==============================] - 181s 223ms/step - loss: 0.9123 - accuracy: 0.6760 - val_loss: 0.8550 - val_accuracy: 0.6915\n",
      "Epoch 16/60\n",
      "813/813 [==============================] - 179s 220ms/step - loss: 0.8946 - accuracy: 0.6845 - val_loss: 0.8578 - val_accuracy: 0.6952\n",
      "Epoch 17/60\n",
      "813/813 [==============================] - 179s 220ms/step - loss: 0.8723 - accuracy: 0.6947 - val_loss: 0.8441 - val_accuracy: 0.7011\n",
      "Epoch 18/60\n",
      "813/813 [==============================] - 179s 221ms/step - loss: 0.8553 - accuracy: 0.6970 - val_loss: 0.7963 - val_accuracy: 0.7129\n",
      "Epoch 19/60\n",
      "813/813 [==============================] - 180s 221ms/step - loss: 0.8362 - accuracy: 0.7102 - val_loss: 0.8008 - val_accuracy: 0.7183\n",
      "Epoch 20/60\n",
      "813/813 [==============================] - 181s 223ms/step - loss: 0.8299 - accuracy: 0.7087 - val_loss: 0.7759 - val_accuracy: 0.7260\n",
      "Epoch 21/60\n",
      "813/813 [==============================] - 182s 223ms/step - loss: 0.8084 - accuracy: 0.7177 - val_loss: 0.7729 - val_accuracy: 0.7286\n",
      "Epoch 22/60\n",
      "813/813 [==============================] - 185s 227ms/step - loss: 0.7981 - accuracy: 0.7215 - val_loss: 0.7571 - val_accuracy: 0.7326\n",
      "Epoch 23/60\n",
      "813/813 [==============================] - 178s 218ms/step - loss: 0.7905 - accuracy: 0.7256 - val_loss: 0.7534 - val_accuracy: 0.7346\n",
      "Epoch 24/60\n",
      "813/813 [==============================] - 180s 222ms/step - loss: 0.7745 - accuracy: 0.7294 - val_loss: 0.7517 - val_accuracy: 0.7361\n",
      "Epoch 25/60\n",
      "813/813 [==============================] - 177s 218ms/step - loss: 0.7597 - accuracy: 0.7317 - val_loss: 0.7486 - val_accuracy: 0.7360\n",
      "Epoch 26/60\n",
      "813/813 [==============================] - 179s 220ms/step - loss: 0.7573 - accuracy: 0.7328 - val_loss: 0.7277 - val_accuracy: 0.7432\n",
      "Epoch 27/60\n",
      "813/813 [==============================] - 180s 221ms/step - loss: 0.7490 - accuracy: 0.7392 - val_loss: 0.7285 - val_accuracy: 0.7418\n",
      "Epoch 28/60\n",
      "813/813 [==============================] - 177s 218ms/step - loss: 0.7394 - accuracy: 0.7399 - val_loss: 0.7293 - val_accuracy: 0.7423\n",
      "Epoch 29/60\n",
      "813/813 [==============================] - 177s 218ms/step - loss: 0.7345 - accuracy: 0.7438 - val_loss: 0.7149 - val_accuracy: 0.7513\n",
      "Epoch 30/60\n",
      "813/813 [==============================] - 178s 219ms/step - loss: 0.7281 - accuracy: 0.7486 - val_loss: 0.7102 - val_accuracy: 0.7547\n",
      "Epoch 31/60\n",
      "813/813 [==============================] - 179s 220ms/step - loss: 0.7210 - accuracy: 0.7471 - val_loss: 0.6986 - val_accuracy: 0.7578\n",
      "Epoch 32/60\n",
      "813/813 [==============================] - 177s 218ms/step - loss: 0.7100 - accuracy: 0.7489 - val_loss: 0.6998 - val_accuracy: 0.7581\n",
      "Epoch 33/60\n",
      "813/813 [==============================] - 177s 218ms/step - loss: 0.7048 - accuracy: 0.7532 - val_loss: 0.6848 - val_accuracy: 0.7595\n",
      "Epoch 34/60\n",
      "813/813 [==============================] - 177s 217ms/step - loss: 0.7002 - accuracy: 0.7544 - val_loss: 0.6904 - val_accuracy: 0.7590\n",
      "Epoch 35/60\n",
      "813/813 [==============================] - 176s 217ms/step - loss: 0.6980 - accuracy: 0.7566 - val_loss: 0.6832 - val_accuracy: 0.7606\n",
      "Epoch 36/60\n",
      "813/813 [==============================] - 176s 217ms/step - loss: 0.6890 - accuracy: 0.7574 - val_loss: 0.6758 - val_accuracy: 0.7667\n",
      "Epoch 37/60\n",
      "813/813 [==============================] - 183s 226ms/step - loss: 0.6854 - accuracy: 0.7602 - val_loss: 0.6809 - val_accuracy: 0.7621\n",
      "Epoch 38/60\n",
      "813/813 [==============================] - 177s 217ms/step - loss: 0.6782 - accuracy: 0.7626 - val_loss: 0.6972 - val_accuracy: 0.7603\n",
      "Epoch 39/60\n",
      "813/813 [==============================] - 177s 217ms/step - loss: 0.6745 - accuracy: 0.7635 - val_loss: 0.6675 - val_accuracy: 0.7650\n",
      "Epoch 40/60\n",
      "813/813 [==============================] - 177s 217ms/step - loss: 0.6717 - accuracy: 0.7638 - val_loss: 0.6660 - val_accuracy: 0.7680\n",
      "Epoch 41/60\n",
      "813/813 [==============================] - 177s 218ms/step - loss: 0.6767 - accuracy: 0.7645 - val_loss: 0.6721 - val_accuracy: 0.7656\n",
      "Epoch 42/60\n",
      "813/813 [==============================] - 176s 216ms/step - loss: 0.6627 - accuracy: 0.7681 - val_loss: 0.6755 - val_accuracy: 0.7670\n",
      "Epoch 43/60\n",
      "813/813 [==============================] - 178s 218ms/step - loss: 0.6573 - accuracy: 0.7700 - val_loss: 0.6738 - val_accuracy: 0.7640\n",
      "Epoch 44/60\n",
      "813/813 [==============================] - 179s 220ms/step - loss: 0.6530 - accuracy: 0.7725 - val_loss: 0.6691 - val_accuracy: 0.7726\n",
      "Epoch 45/60\n",
      "813/813 [==============================] - 177s 218ms/step - loss: 0.6543 - accuracy: 0.7727 - val_loss: 0.6640 - val_accuracy: 0.7698\n",
      "Epoch 46/60\n",
      "813/813 [==============================] - 178s 218ms/step - loss: 0.6529 - accuracy: 0.7719 - val_loss: 0.6609 - val_accuracy: 0.7690\n",
      "Epoch 47/60\n",
      "813/813 [==============================] - 177s 217ms/step - loss: 0.6458 - accuracy: 0.7735 - val_loss: 0.6585 - val_accuracy: 0.7676\n",
      "Epoch 48/60\n",
      "813/813 [==============================] - 177s 218ms/step - loss: 0.6467 - accuracy: 0.7737 - val_loss: 0.6543 - val_accuracy: 0.7736\n",
      "Epoch 49/60\n",
      "813/813 [==============================] - 175s 216ms/step - loss: 0.6369 - accuracy: 0.7772 - val_loss: 0.6442 - val_accuracy: 0.7733\n",
      "Epoch 50/60\n",
      "813/813 [==============================] - 177s 218ms/step - loss: 0.6366 - accuracy: 0.7784 - val_loss: 0.6590 - val_accuracy: 0.7684\n",
      "Epoch 51/60\n",
      "813/813 [==============================] - 176s 217ms/step - loss: 0.6356 - accuracy: 0.7789 - val_loss: 0.6415 - val_accuracy: 0.7740\n",
      "Epoch 52/60\n",
      "813/813 [==============================] - 177s 218ms/step - loss: 0.6317 - accuracy: 0.7762 - val_loss: 0.6472 - val_accuracy: 0.7749\n",
      "Epoch 53/60\n",
      "813/813 [==============================] - 176s 216ms/step - loss: 0.6328 - accuracy: 0.7801 - val_loss: 0.6440 - val_accuracy: 0.7738\n",
      "Epoch 54/60\n",
      "813/813 [==============================] - 177s 218ms/step - loss: 0.6237 - accuracy: 0.7813 - val_loss: 0.6377 - val_accuracy: 0.7789\n",
      "Epoch 55/60\n",
      "813/813 [==============================] - 177s 218ms/step - loss: 0.6242 - accuracy: 0.7805 - val_loss: 0.6483 - val_accuracy: 0.7776\n",
      "Epoch 56/60\n",
      "813/813 [==============================] - 176s 217ms/step - loss: 0.6199 - accuracy: 0.7835 - val_loss: 0.6527 - val_accuracy: 0.7723\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 57/60\n",
      "813/813 [==============================] - 178s 219ms/step - loss: 0.6182 - accuracy: 0.7837 - val_loss: 0.6578 - val_accuracy: 0.7704\n",
      "Epoch 58/60\n",
      "813/813 [==============================] - 179s 220ms/step - loss: 0.6137 - accuracy: 0.7844 - val_loss: 0.6412 - val_accuracy: 0.7796\n",
      "Epoch 59/60\n",
      "813/813 [==============================] - 208s 256ms/step - loss: 0.6095 - accuracy: 0.7864 - val_loss: 0.6394 - val_accuracy: 0.7827\n",
      "Epoch 60/60\n",
      "813/813 [==============================] - 209s 256ms/step - loss: 0.6139 - accuracy: 0.7856 - val_loss: 0.6563 - val_accuracy: 0.7753\n"
     ]
    }
   ],
   "source": [
    "model = define_cnn_gru_model(word_index=word_index, \n",
    "                             max_sent_len=max_sent_len,\n",
    "                             embedding_matrix=fasttext_embedding_matrix,\n",
    "                            )\n",
    "\n",
    "#earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=3, verbose=0, mode='auto')\n",
    "history = model.fit(x=x_train_seq, \n",
    "                    y=y_train_en, \n",
    "                    batch_size=batch_size, \n",
    "                    epochs=epochs, \n",
    "                    verbose=1, \n",
    "                    validation_data=(x_valid_seq, y_valid_en),\n",
    "                    #callbacks=[earlystop],\n",
    "                   )\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AmeMg4wTIHOumIsDNGdOZ3JcuhbVrXcuO1wtTp7qa+5QpcPLJbsnNjW3RjDHRcdw19N5gCf0wNj8Cmx6Cxk3QFnGbJE8SFMyGEZdB4aWQ3nW1tLYW3n7bJfe334ZVqz56h6Xhw9087med5Wr0JSWQltZ3RTLGRIcl9HjWvt9NCtawEWqWw64XoKHMHcuZAsM/CTknQcZYtyTnggiqbkrfDz5w3SRXrXJdIsvCT/X5XE2+pMRNAdy5WE3emP7NEnqiqd8Au/7qlqqloBFDTv3ZrmskHgi1uakJQq0QaofkPNqSTmBX3VhWbzuBpavG8ufXprN996ADTx861DXRdDbbTJsG48bZhVdj+gtL6Iks0AJNW6FhMzRudrX5pm2AgifZtbl7k11zTevervOCLQCoL53God+ktOlG3l83jDVrXK1+zRpob3dvkZ7uJhnLzYWcnK4lP98l/NNPdxdnjTG9zxK6+ShVaNkNDRtg8+9h+5Mgfhj7VZj0PcgYTXs7rF/v7qW6YoVrqtm/37XV79/vls4RrSJuGoMzzoDp093UwYWFrt0+O9sdN8ZEhyV0c3gNm2H9L2DLY675Ju9M11QTaHSjXQONbs6avJkwZDYUzEazT6Jmv4f33oN33+1aag6aJCItzSX3ggJXw8/L61oXFcGkSTB+vJsGwRhzZJbQTc8074L1v4SaUvBlgD8DfOnucUcDVC52zTsASYMhbwYk5Rxo2lFPCrWNaVQ2F7OjdhIbdk9k665B7NoFVVWu182+fW5pa+t6WxF3C79Jk1zTzvjxXcuwYVbDNyaSJXQTPU3bYe8bUPm6S/yBZgi2uguvwVbXNq8RE76nDIGsiZCcB95U8KWh3jQ6QqnU1CVTWeVjb6WP3Xv9VOz2Ub47icaWFFrbU2jtSEF8KfjTc9lRfzJ4U0hKguRkSElxtfyCArfk57v10KFuyc+3C7kmMVlCN30nFHQXZevXQ/2HUBded9S6C7jBZpf0gy1uIFUPdQT9bN0/hfWV01mzezpryk9mX42PutoQwUAIjyeER8KLJ4TPEyJ3cIjcPKVNhtPIaFLTk8jIcNMnZGfDoEFdF3gHDXLL4MFuSUrqvR+RMcfDErrpn1RBAxAKgHa4dWdXy8haf0sFVL8H1cvcOtBw1G8VCHmpqB3FpspxbKgYx67qITS0ZNLYmkFjWwaNrRnsa8hjR3URe+uGkJ7uYfBgV9MfMQJGjuxaDxvmvhAyM7uWtDRrGjJ943AJ3ebtM7Ej4nrXePzAEa6KjvyMW2sI6svcfwAA4gE84bWAeN3jzkUVmsvxNWykKLyc1/DWYb8Uguqntn0ElU1F7GvIo701SHtbkNC+IN6aII3v+1m1dywbdk+gbM94NuyewO7aQrIyQwzNbWBobh0FOXXkZjcSlHRadTAtoVzwppKUJOTkwJAhrmloyBC35OS4L4XU1K7F48F9yTVuhfp1ULcOOupg6AWQ/wnw2r8R5qOshm4GpmA7BJugo9El944GaKuC5p3QtMMtzTugvSb8JeElGPLS3uEj0N5KamATPpoOvFxA/fjk8E1IbYFk9jfnUtMwmMq6XPY15lHdkEt1Yy4h9ZCZ0uCWVLcuHLybcUM3kOLvuoIcDPnwegK0BrPY0nIx2wOXsFcuhKQckpK9+JN8JKd4SPIrbS2ttDXW0dFUS7C1jlBbHXnZTRQObWFYQQt5g1rw0YIbs+AH8XWtvSkRF8YjFn8m+DLtyySGrMnFmGhThZZdbtRu/QZ3sdibCknZbrSuP8slvmCTm4+nvcatw49DrdUEm/ZBezXeYDWgBDSD9lAmbaFMWoOZ1LUWsK12MmV7J7N252RWbJ5IzX4fpxe9xoWT/8q8U15g+KDuZ6kOhjx4Pb13N/IgyXRoJh1kECKVkCQTkhRCkoJ6kvH6k/EnJ+FPTiI5NRmPLyl8UTy9a/GmuYvlqcMhrdBdQPeEGw3aatwUF/Vlbh1oguzJkHMyZJ/kvmiiLdQB7bVdXXUDTV2POxrCX/z17jEKWRMg+0R30d93hImRVKF1D9Sthdo1MGiq6wJ8DCyhG9Ofdf4NHmUjfEe70rbnfUJ73yTY3kqwI0AwECQYCBAKBvEkZeBLyyE5I5vkTLfe35jO9p2pbNmRyqatqZRtSqGyykNzU4CWpg5amgK0NAcItreQntJERnIjGSluifzvIfJxsr+NFH/rgSU1qYUkX7tbvG6dktRGir+F9OSmQ37RhFRoDAzB5+kgzds1s1xQfYQ0Cb+n+cC++lAxDTqGJG8zyVJHktTh0zq82oTiD3+5pB74ksGfhTd1EP70HDzJg9yXbqDBddVt2QXN5dBaCfQkH4r7XR3ozSWQMcYleG+a+1ISf/jLSdwke7Vr3Jd6p4k3wql3H9Xv+8C7Wxu6Mf3YMV5N9ScJ/qLToOi0Hj9ncAEMHgPTjnCeKrS0QFMTNDa6dVubm9TN6+1aezwQDLolEHBLeztU1rlBZtXVbl1T427Q4vUqSd42UnxNJPua0JZ9hJp24W2vIEUryPbvoj3go2z3eMr2jKds93i2VhUTDHkZlbedk0euPrCMyttOY2sGdS3DqWvOpr4li8bWDPzeDvflktT1BZOdWkdOei05advJzdxPdmotLYF0KhtGUNlYSGXDNCobC6lvyyPkyUC97r8I8btxGAHJJEAmIW8m6kkjOSlIYdZGCjPWMiRlHbkta8ls3YRP2vBKBx4J4KEDIUggpZjWnM/S6D2RWk5iX8eJDEkrYMIx/dYPz2roxph+IxRyXxyqXUvn/lCo68uj8wukpQWam7uWlhb3JdP5ZeP1uu/LmhrYu9cte/a4dXt7+Lp8xNLR4b68IpeWlq4vq84ldJytWTffDHfeeWzPtRq6MSYueDzxMQ1EIPDR/14aG93S3Oy2O9ctLW4gXHr6R5fRo3snLkvoxhhzlHw+NxYhOzvWkXyUJ9YBGGOMiQ5L6MYYkyBidlFURKqA7cf49DxgXxTDiTUrT/+VSGWBxCpPIpUFel6eUaqa392BmCX04yEipYe6yhuPrDz9VyKVBRKrPIlUFohOeazJxRhjEoQldGOMSRDxmtAfinUAUWbl6b8SqSyQWOVJpLJAFMoTl23oxhhjPi5ea+hmABOR10Vkv4gkxzoWY/oTS+gmrojIaGAWblq8y/rwfW1Uten34i6hi8gcEdkgIptE5JZYx3O0ROQREakUkTUR+waLyCsisjG8HhTLGHtKREaKyCIRWS8ia0Xk+vD+3izPl4F3gMeAaw6K5RkRqRKRahH5dcSxr4djbBCRdSJyani/isgJEef9r4jsEpFVIrJVROpF5GYR2QvsEJHN4dffF/4P4QURGRHx/MEi8qiIVISPPxvev0ZELo04zx9+jalR/Ll0S0S8IrJCRF6IiDHuPmsAIrJNRFaLyEoRKQ3vi8vyiEiOiCwQkQ/Dn80Z0ShLXCV0EfECvwHmApOBL4jI5NhGddQeA+YctO8W4DVVHQe8Ft6OBwHgRlWdBJwJXBv+ffRmeb4M/Cm8XCwiQ8KfixdwA9VGA4XAUwAicgXww/DzsnC1+uqPvaoTBB5X1VOAfwEygZOAx4H7genA34E/AEVAC/DriOf/L5AGnAgUAP8d3v9H4EsR580DdqvqymMo/9G6HlgfsR2vn7VO56rq1Ij+2vFanl8Bf1PVicApuN/R8ZdFVeNmAWYAf4/YvhW4NdZxHUM5RgNrIrY3AMPCj4cBG2Id4zGW6zngwt4qD3A20AHkhbc/BP49/LmoAnzdPOfvwPWHeD0FTojYfgz4r/Dji4EQrnmn2/IAU4H9EftDwKBu3mc40ABkhbcXAN/rg9/HiHBiOA94Id4/a8C2zt99xL64Kw+uYrGVcKeUaJYlrmrouJrXzojt8vC+eDdEVXcDhNcFMY7nqIXbtqcB79J75bkGWKiqncOjnwjvGwlsV9VAN88ZCWw+ivcQEVkJPAs0qeoSwuURkTRcbf8EEakHFgM54f8QRgI1qrr/4BdU1QpgKfBPIpKD+w/zT0cR07G6F/ge7oumUzx/1hRYKCLLRWR+eF88lmcMrgLyaLg57GERSScKZYm3Cz3d3drF+l3GmIhkAE8DN6hqvRzjHXiO8B6pwOcAr4jsCe9OBnKAvUCRiPi6Seo7gbGHeNlmXBNJp6FAuapOFZFLgAUiclLE8RuBCUCDquaE28BX4D6XO4HBIpKjqrXdvNcfcM04PuBtVd3Vs5Ifm3D8laq6XERm9+Z79aGZqlohIgXAKyLyYawDOkY+4FTg26r6roj8iig1FcVbDb0cVxPqNAKoiFEs0bRXRIYBhNeVMY6nx0TEj0vmf1LVZ8K7e6M8n8K1cU/GNXVMBSYBS8LHdgN3iki6iKSIyMzw8x4GbhKR08Q5QURGhY+tBK4KXzicA5wT8X6NQBvuekdneTJxtd1KERkM/KDz5HCN6mXgfhEZFL7w+YmI13sW90d8Pa5NvbfNBC4TkW246wnnicjjxPFnLfyfDqpaCfwFd00jHstTjqs4vBveXoD7bBx3WeItob8HjBORYhFJAq4Eno9xTNHwPF09Nq7BtUX3e+Kq4r8H1qvqPRGHeqM81wCPquoOVd3TueAuSn4BuBQ4AdiB+4P5PICq/j/gJ7jmmQZcYh0cfs3rw8+rBb6IS8idfduTgBRcO31nee7FXf8Yjetp87eDYrwa18b/Ie6P8YbOA6ragvviKwaeoZep6q2qOkJVR+P+Tv6hql8ifj9r6SKS2fkYuAhYQxyWJ/y53SkinbcVPR9YRzTKEusLBMdwQWEeUIZrF7091vEcQ/xP4mqTHbjE8zUgF3fxamN4PTjWcfawLGfjmrw+wNV2V4Z/P/Fanim4JpQPcMnijvD+qJQHuAPXi6avyzWbroui8fq7GQOsCi9rO//247g8U4HS8GftWWBQNMpiQ/+N6QPhJpoVwNWqujjW8ZjEFG9NLsbEHRH5Ou6i6cuWzE1vshq6McYkiB7V0OUIw+1FJFtE/hoeMr1WRL4S/VCNMcYczhFr6OFBE2W4EYDluJ4mX1DVdRHn3AZkq+rNIpKPG/E0VFXbD/W6eXl5Onr06OMvgTHGDCDLly/fp4e4p2hPBhZNBzap6hYAEXkKuBzXzaaTApnhbmwZQA1uno9DGj16NKWlpT14e2OMMZ1EZPuhjvWkyaUnw+1/jRvkUQGsxs2dEcIYY0yf6UkNvSfD7S/G9UE+DzfM+hURWaKq9R95ITf/wnyAoqKio4/WGGP6IVVob4eGBqiv71o3NoLHA34/JCV1rYcOhWHDoh9HTxJ6T4bbfwW4U12D/CYR2QpMBJZFnqSqDxG+b15JSYl1rzHGfIwqtLZCS0vX0tYGgQAEg24dCLhEOWgQ5Oa6tdfb9fzaWti1C8rLoaLCJdbO1wq1N5CuW/B52hFvCupNRbypiD+V2qZs9lZ6qaqCykqoqnKJORRyr6vqHodC0NHRtRQO2sEZY99l2KDdFGRVkp9ZRUFWJYMzati09wTe23I6pVtKWL3zZNoDydx8M9x5Z/R/dj1J6AeG2wO7cMOIrzronB244atLRGQIbgKjLdEM1BjTd1RdIqupgZrqEG2V66BxK606mDZyadU82kKDUPGSlORqncnJbu3zucTZ2OiWpia3DgTcyPQ0zx7yfOvI9a8j1NZIXb2H+noPtfUe6uo87K7OYXPlWLZUjqFi/3BUO1uGlaE5e5gwbAMThm1gVN52UvytJPnaSfG3kZHWTmpKBw1NSTS1JNPakUJrRwqBoI/Cwbs4dcgmThiyiaE5ew9Z7raOJLbljKV8yHgqWydQFxpPsxTRFsqiPZRJeyiTtlAmyb5mJg1+nfHZixib+Q9yk7rSXUg9tEs+Hd4C1JfFWR3P87XZj7hj+Kn3TKFp+Ldwg8Sj64gJXVUDInIdbl5pL/CIqq4VkW+Gjz8A/CfwmIisxjXR3KxdU5waY7rTWgV1a6B2LRCC9NGQUQzpxeDPOPLzQwFCjTsJ1G2D1iq0tcq9ZlsVtNcQkjQC3kEEJIcOyaFNc2hsCNFQ20JzfTMtjc20NTfT0JTCntp8KqoL2FlVwI69Bfi1hrPGLWHWhCXMHL+U3Myaj799SKhvyaItkExH0E97IImOgJ/2YBJJ7ZoLM5gAABmNSURBVKn429JIbk8jtT2NzI5kRuVtZ3LhOgZnHDTD8GEmwA5oCo0UE/Kkk6ll+OlqxQ3hI0gKgVAyHaEk2gPJBII+knwdJPta8Xvb8EorHm0nlDIc0sfiyb4EMk+AzLHgTYVgCwRaCAVaCba34G+vYEJjGRMayqDhZQgdsqOe48+GgnNgyL9BwSxIK8KTPJgU8ZDSeY4qNO+A6vfw1JSSU11KzrCOI/9+j0HMBhaVlJSo9XIx/ZKGoK0G2iqho95ta9CtCUEoEE4EzRBsdutQKyAgPhAveLxuHWyFQNOBJdDWiNZvxtuwBk9H1SFDaArksr+1kMa2TBpaMqlrzmR/Yybt7UJhzjZG5W5h5OAd+LzBjz23umEwNU2DSU1qYVDaftJTmg/5PsGQB6/n0P0X6nQctf5ZtGfPgqxJ+KnDF9yHL1SNL7APT3A/GmwnFOhAg+0Q7IBQGz5PK0meZnzSjJdmvNqCpo5As05EsyZD1mTInowvbTBC+Ofa+XNuq4bGzeFlCzRshkAjZI6HrAmQNdGt00aA9OJg91DQJeLmcuhogEBD1xoP5M+EQdPc77oPichy7bpj00fE23zoxhyfYDvULHfJonUPtOyJWO91tdu2fS6xRPNtQx6a2tJpbktj277RrC2/lDU7T2LtrhNZW34iHUE/o/O2UVywleL8rRQXbKMofzeDMhrIStvH0CFbSR/ZgNcToqZ9NPs7zmRbzVXUh8bQxChadQit5NNGLuLx4fGBzwP+ICS3t5PmryXNV0tOjpe8oWnkD0sjJzcNr9fvfiZt+9wXWGulq+V7UyB/JtmpQ8mO6k+iB5JyXA061jxe9x9TRnGsI+kxq6Gb+BMKQksFeJLAlwbetEPXkkIdUF0Kla+jexdB1VIk2FVjbQ+lUN00jL11Q9hTO4S6tgLq2wtoChTQHCqgNZhNU7OXpmYPTc0eGpu81NV7qW1Mo7ktjeb2NFraUxFvChkZSnpakIz0AOlpQdJSg6SkpZCcnk56VjLZ2UJ2NqSlcaDdubPnQ0YG5OdDXp5bsrOhF+4TYhKA1dBN/xYKQjDcLBHqwDVdiFsj0L7f1aprlkNNKexf6Zo6IgQ1ifZQCqjikSAeCeAhiNfTVdNeU34yi9Z+jTfWn8PqnSezp24o+DIZO1YYOxaysqC52V3E61wHgy7ZZmZCRh4UFMO4bCgq+ugyaJAlYBN7ltBN9Ki6RNu8C5p3urbHzqV9P3TUuTbpziXQEE7iR7jwFNYWTGNn4zS21n2dTVWT2LM7SHN9M6lJzaQmtZCR2oLfJ4QIt2OLF8XH7tYp7NVPkF2QT+FU+Pwn4XujYOxY1+XNErFJFJbQzdFp3w/7lkH1Mqj94ECPCtqq3RJq+9hTNGkwAW8ercEsmtqzqW8Zwv6GTKrrM9lXm87earc0tqbTEfAjom7BrRtbM1izaxrb908gKdlLcrKrMU+eDFOmw8knw5QpUFzs+iYbM1BZQjfdC7a63gUNZW6pXQPV70LDxvAJ4rp/pQyFjLEEc06nYl8um8sHs3VvIZsrRrB260hWlRWyfVcqoYM6UmRnw8iRUFgIw4dD4TQ4oRCGDHHNFzk5XUtmZtegEWPMoVlCN06gBXb/HXYugKql0LSdyBkemkLDqWY6df6v0JwynY6sElqD2SxdCm+8AW+/7Ub3gWvG6EzU513k1iNHuqWoyK0zM2NTTGMSmSX0gayjHnYvhB0LoOIF156dNBiGXkBD3jW8sXI8jz83npfeHE9DS1a3LyECU6fCt74Fs2fDrFmuhm2M6XuW0BNdoAmaK6Bll2s6qVsHdeuhfp27WAm0ST7bAl9kXcNnWVM1mzd/5+fVV918FWecAXfeDfPmuR4fkb1AQiEoKXHNIsaY2LOEnggihhZTUwo1K9x2S4WrhUcIShq7myayYsts3l47ibc3zWDJh7MIhro+CmPGwG23wdVXw/jxfV0YY8yxsoQer1r2QvkzsOsF1+OkLTx1jscP2SdD9kkE8i9iV81wVm8ezjsrh/HMKyfw4c4ifD4Ps2bB3Llw+7ddE0nnhcisLDe5kjEm/tifbjxp2QM7n4Ed/w+qFru5LzLHQeGlNKeezpbaElZsm8Ka15N5+21YtsxNOwowbhycPw/unAvnnecGyxhjEosl9P4oFOiaha9+HdStdY8bNwMKWROpHno7z664gsd/dxLr1wt7I2YE9fngtNPguutg5ky3FBTErDTGmD5iCb0/UYWdT8Oq27r6e4sPssajg6ayO/Uann73Uzz41ImsXeuGN06bBp/8JEyYABMnuqW42M0PYowZWCyh9xd734CV33Pt4dknEjrjj2ysPo1X3z2BRU8nsXixu3uKx+O6Bv7qV/CpT7l+3cYYA5bQ+9b+D7qaTVTD6yBs/V+oeBFNHcHK5Ef45RNf5uW/eakJ31Ng1Ch3AXP2bLjkEjcrnzHGHMwSel9oq4aVN8Pm33d7uENyeKbs51z/m2+zd18qgwfDZZfBuefCOee4hG6MMUdiCb03qbra94ob3aRWk74Lo76A4mH1auGFF4WX/yas3DgKT1Imn/40XHklnH++tYEbY46eJfTeUr8B3vtX2PsPyD0Tpj/Ilv1T+ON98PjjsHkzpKS4mvhN/wlz5rib7BpjzLGyhB5NoSDsWQibHnQDfnyZcPoDrG37Oj+51sP//Z+rtJ93Hnz/+/CZz7iBPMYYEw09SugiMgf4FeAFHlbVO7s5ZzZwL+AH9qnqOVGMs39rrnDt45sfdkPuUwpg4o2sDf47P7xtKE8/7W47duON8G//BiNGxDpgY0wiOmJCFxEv8BvgQqAceE9EnlfVdRHn5AD3A3NUdYeIDIxhLPUbYd1PXTu5BmHI+XDq3SyruJyf/iiJ555zNfDbboMbbnD3ijTGmN7Skxr6dGCTqm4BEJGngMuBdRHnXAU8o6o7AFS1MtqB9it162DNT2DHU+5GxeOuRcd/m1feOYGfXQOvv+7mRvnhD12N3KaTNcb0hZ4k9EJgZ8R2OXDGQeeMB/wi8jqQCfxKVf948AuJyHxgPkBRPI6IadrpeqzsWADeVJj4HULjb+Tpl4bys/mwYoW7scM998DXv27zpRhj+lZPEnp3t9DVg7Z9wGnA+UAq8LaIvKOqZR95kupDwEMAJSUlB79G/7bnH7D08xBsgcm3wMR/Z+POfOZf4mrk48fD738PX/yi9VYxxsRGTxJ6OTAyYnsEUNHNOftUtQloEpHFwClAGfFOFT78pRsYlDURZj1DIG0Cv/yla1JJToYHH4Svfc3ue2mMia2e3CP9PWCciBSLSBJwJfD8Qec8B8wSEZ+IpOGaZNZHN9QY6GhwtfIV34URn4GL3mHF5glMnw633OKG469bB/PnWzI3xsTeEWvoqhoQkeuAv+O6LT6iqmtF5Jvh4w+o6noR+RvwARDCdW1c05uB97ra1bD0Sqj/EKbdRXPRjfz4P4S773ZzqSxYAP/0T7EO0hhjuohqbJqyS0pKtLS0NCbvfVjNu+CDO2DrY+6GyTP/j9fWnsc3vuFGd371q3D33dZzxRgTGyKyXFVLujtmI0U7ddTDul/Ah/eABmD89dQMv53v3JLLH/7g7vizaJGb8dAYY/ojS+jgBga9fyO0VcGoK+GUn/LiG8V8ZS7s3+8GBn3/+5CaGutAjTHm0AZ2Qg91wPs3Qdl9kD8TTn0Rck/n/vvh29+GKVPg1Vfd2hhj+ruBm9DbquHNz7nZECf8O0z7BSF83PI9uOsuuPRSePJJSE+PdaDGGNMzAzOh166GNy6Hll1w5mMw5hpaW+Gaa+DPf4Z//Ve47z7rimiMiS8DL6Hv/Au8fTX4s+CCxZB3BtXV7v6cb74Jv/gF3HQTSHfjY40xph8bOAldFdbf5UZ85k6HWX+BtOG8/z5ccQWUl8NTT8HnPx/rQI0x5tj0ZKRo/At1wLL5LpkXfR7Ofx1NHc6DD8JZZ0F7u5uPxZK5MSaeJX5Cb6+F1+e5m0+ceDvMfILG1lS+9CX45jddv/IVK2DGjFgHaowxxyexm1wat8Ibl0DDRjjzURjzz6xbB5/9LGzYAP/5n66PuSfxv9aMMQNA4ib0jgZ45WwINMO5C2HIbPbudTVyEXjlFXdvT2OMSRSJm9A3PQQtFXDhm5A/E1U3xW1DAyxfDpMnxzpAY4yJrsRM6ME2N4f5kPPcCFDgoYfgxRfhV7+yZG6MSUyJmdC3/gFadsOM/wWgrAy+8x248EK47roYx2aMMb0k8S4HhgJu1sTBp8OQ8+jogKuvdncWevRRuwBqjElciVdD37EAGjfDrLtAhJ/8BJYtc0P6CwtjHZwxxvSexKqvqsK6n0HWJBhxOe++C//1X66GfsUVsQ7OGGN6V2LV0CtegtoP4MzHCKmHL38ZRoyA//mfWAdmjDG9L7ES+rqfQVoRjL6KNWvcxdBHH4Xs7FgHZowxva9HTS4iMkdENojIJhG55TDnnS4iQRH5bPRC7KHKJVC1FCZ9Fzx+Fi92u889t88jMcaYmDhiQhcRL/AbYC4wGfiCiHysJ3f4vJ8Df492kD2y9meQnA9jvwrA4sVQVASjRsUkGmOM6XM9qaFPBzap6hZVbQeeAi7v5rxvA08DlVGMr2cat8Dul2HC9eBLQxWWLIFPfKLPIzHGmJjpSUIvBHZGbJeH9x0gIoXAp4EHohfaUahd7dbDLgJg0ybYs8cSujFmYOlJQu/u3j160Pa9wM2qGjzsC4nMF5FSESmtqqrqaYxH1rDRrTPHARxoP581K3pvYYwx/V1PermUAyMjtkcAFQedUwI8Je6+bXnAPBEJqOqzkSep6kPAQwAlJSUHfykcu/oy136elAO4hJ6fDxMmRO0djDH9REdHB+Xl5bS2tsY6lF6VkpLCiBEj8Pv9PX5OTxL6e8A4ESkGdgFXAldFnqCqxZ2PReQx4IWDk3mvath4oHYOXe3ndl9QYxJPeXk5mZmZjB49GknQP3JVpbq6mvLycoqLi4/8hLAjNrmoagC4Dtd7ZT3wZ1VdKyLfFJFvHnPE0dRQBlnjAdi5E7ZuteYWYxJVa2srubm5CZvMAUSE3Nzco/4vpEcDi1T1JeClg/Z1ewFUVf/5qCI4Xh2Nbt7zTJfQlyxxu+2CqDGJK5GTeadjKWP8z+XSuMmtIy6IZmXBlCkxjMkYY2Ig/hN6fZlbR9TQzz4bvN4YxmSMSVi1tbXcf//9R/28efPmUVtb2wsRdYn/hH6gy+IJVFXBunXWfm6M6T2HSujB4GF7bfPSSy+Rk5PTW2EBiTA5V0MZpI0AXxpvvul2Wfu5MQPDDTfAypXRfc2pU+Heew99/JZbbmHz5s1MnToVv99PRkYGw4YNY+XKlaxbt45PfepT7Ny5k9bWVq6//nrmz58PwOjRoyktLaWxsZG5c+dy9tln89Zbb1FYWMhzzz1HamrqcceeGDX0iPbzlBQoKYlxTMaYhHXnnXcyduxYVq5cyV133cWyZcv4yU9+wrp16wB45JFHWL58OaWlpdx3331UV1d/7DU2btzItddey9q1a8nJyeHpp5+OSmyJUUMf6SZ3XLIEZsyApKQYx2SM6ROHq0n3lenTp3+kr/h9993HX/7yFwB27tzJxo0byc3N/chziouLmTp1KgCnnXYa27Zti0os8V1Db6uBtmrIHEd9PaxYYe3nxpi+lZ6efuDx66+/zquvvsrbb7/NqlWrmDZtWrd9yZOTkw889nq9BAKBqMQS3wn9wAXR8bz1FoRC1n5ujOldmZmZNDQ0dHusrq6OQYMGkZaWxocffsg777zTp7HFd5NLxKRcixeDzwdnnhnbkIwxiS03N5eZM2dy0kknkZqaypAhQw4cmzNnDg888ABTpkxhwoQJnNnHCSnOE3oZiAcyxrBkCZx2GkT892OMMb3iiSee6HZ/cnIyL7/8crfHOtvJ8/LyWLNmzYH9N910U9Tiiu8ml/oySB9NS3sSy5ZZc4sxZmCL74TesBEyx7N0KbS3wznnxDogY4yJnfhN6KquySVzPC+/7Loqzp4d66CMMSZ24jeht+6FQCNkjuPll13t3NrPjTEDWfwm9AY3KdeelvGsXw/z5sU4HmOMibE4Tuiuy+Kr77hh/3PnxjIYY4yJvfhN6PVl4EliwctFFBfD+PGxDsgYMxAc6/S5APfeey/Nzc1RjqhL/Cb0ho2E0sfyyqte5s2z+4caY/pGf07o8TuwqKGMfW3jaW625hZjBqzlN8D+KM+fO2gqnHboWb8ip8+98MILKSgo4M9//jNtbW18+tOf5kc/+hFNTU187nOfo7y8nGAwyH/8x3+wd+9eKioqOPfcc8nLy2PRokXRjZt4TegagoZNrK2YS3IynHturAMyxgwUd955J2vWrGHlypUsXLiQBQsWsGzZMlSVyy67jMWLF1NVVcXw4cN58cUXATfHS3Z2Nvfccw+LFi0iLy+vV2LrUUIXkTnArwAv8LCq3nnQ8S8CN4c3G4FvqeqqaAb6Ec07IdTGotLxzJ4NaWm99k7GmP7sMDXpvrBw4UIWLlzItGnTAGhsbGTjxo3MmjWLm266iZtvvplLLrmEWX00DewRE7qIeIHfABcC5cB7IvK8qq6LOG0rcI6q7heRucBDwBm9ETBw4D6ii1eO49Pf6LV3McaYw1JVbr31Vr7xjY8nouXLl/PSSy9x6623ctFFF3HHHXf0ejw9uSg6HdikqltUtR14Crg88gRVfUtV94c33wFGRDfMg4S7LJbtHm/t58aYPhU5fe7FF1/MI488QmNjIwC7du2isrKSiooK0tLS+NKXvsRNN93E+++//7Hn9oaeNLkUAjsjtss5fO37a0C3042JyHxgPkBRUVEPQ+xGQxktHemkDh7GuHHH/jLGGHO0IqfPnTt3LldddRUzZswAICMjg8cff5xNmzbx3e9+F4/Hg9/v57e//S0A8+fPZ+7cuQwbNqxXLoqKqh7+BJErgItV9V/C21cD01X1292cey5wP3C2qn78RnoRSkpKtLS09JiCDr72Sda8V8HDu1bwP/9zTC9hjIlT69evZ9KkSbEOo090V1YRWa6q3d45uSdNLuXAyIjtEUDFwSeJyBTgYeDyIyXz49VaVcaHFdbcYowxkXqS0N8DxolIsYgkAVcCz0eeICJFwDPA1apaFv0wI4Q6SAluZWvVOJtd0RhjIhyxDV1VAyJyHfB3XLfFR1R1rYh8M3z8AeAOIBe4X9yQzcCh/iU4bo1b8UoQ3+Dx1l3RmAFKVZEEHx5+pObw7vSoH7qqvgS8dNC+ByIe/wvwL0f97sdgz8YyhgJFJ9rVUGMGopSUFKqrq8nNzU3YpK6qVFdXk5KSclTPi7uRou+vH8bORfO54KYJsQ7FGBMDI0aMoLy8nKqqqliH0qtSUlIYMeLoeoAfsZdLbzmeXi67dkFhYZQDMsaYOHC8vVz6HUvmxhjzcXGZ0I0xxnycJXRjjEkQMWtDF5EqYPsxPj0P2BfFcGLNytN/JVJZILHKk0hlgZ6XZ5Sq5nd3IGYJ/XiISGmv9XOPAStP/5VIZYHEKk8ilQWiUx5rcjHGmARhCd0YYxJEvCb0h2IdQJRZefqvRCoLJFZ5EqksEIXyxGUbujHGmI+L1xq6McaYg1hCN8aYBBF3CV1E5ojIBhHZJCK3xDqeoyUij4hIpYisidg3WEReEZGN4fWgWMbYUyIyUkQWich6EVkrIteH98dreVJEZJmIrAqX50fh/XFZHnA3eReRFSLyQng7nsuyTURWi8hKESkN74vL8ohIjogsEJEPw38/M6JRlrhK6CLiBX4DzAUmA18QkcmxjeqoPQbMOWjfLcBrqjoOeC28HQ8CwI2qOgk4E7g2/PuI1/K0Aeep6inAVGCOiJxJ/JYH4HpgfcR2PJcF4FxVnRrRXztey/Mr4G+qOhE4Bfc7Ov6yqGrcLMAM4O8R27cCt8Y6rmMox2hgTcT2BmBY+PEwYEOsYzzGcj0HXJgI5QHSgPdxN0SPy/Lgbhf5GnAe8EJ4X1yWJRzvNiDvoH1xVx4gC9hKuFNKNMsSVzV0oBDYGbFdHt4X74ao6m6A8LogxvEcNREZDUwD3iWOyxNuolgJVAKvqGo8l+de4HtAKGJfvJYFQIGFIrJcROaH98VjecYAVcCj4eawh0UknSiUJd4Sene3J7F+lzEmIhnA08ANqlof63iOh6oGVXUqrnY7XUROinVMx0JELgEqVXV5rGOJopmqeiquyfVaEflErAM6Rj7gVOC3qjoNaCJKTUXxltDLgZER2yOAihjFEk17RWQYQHhdGeN4ekxE/Lhk/idVfSa8O27L00lVa4HXcdc74rE8M4HLRGQb8BRwnog8TnyWBQBVrQivK4G/ANOJz/KUA+Xh//4AFuAS/HGXJd4S+nvAOBEpFpEk4Erg+RjHFA3PA9eEH1+Da4vu98Td0PH3wHpVvSfiULyWJ19EcsKPU4ELgA+Jw/Ko6q2qOkJVR+P+Tv6hql8iDssCICLpIpLZ+Ri4CFhDHJZHVfcAO0Wk8z6a5wPriEZZYn2B4BguKMwDyoDNwO2xjucY4n8S2A104L6pvwbk4i5ebQyvB8c6zh6W5Wxck9cHwMrwMi+OyzMFWBEuzxrgjvD+uCxPRLlm03VRNC7Lgmt3XhVe1nb+7cdxeaYCpeHP2rPAoGiUxYb+G2NMgoi3JhdjjDGHYAndGGMShCV0Y4xJEJbQjTEmQVhCN8aYBGEJ3RhjEoQldGOMSRD/H3+2alIl2kouAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curves(history, acc='accuracy', val_acc='val_accuracy')\n",
    "\n",
    "model_dict['cnn_gru_fasttext'] = model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 564,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_19\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_10 (InputLayer)        [(None, 452)]             0         \n",
      "_________________________________________________________________\n",
      "embedding_63 (Embedding)     (None, 452, 300)          8100      \n",
      "_________________________________________________________________\n",
      "conv1d_47 (Conv1D)           (None, 452, 100)          120100    \n",
      "_________________________________________________________________\n",
      "max_pooling1d_47 (MaxPooling (None, 113, 100)          0         \n",
      "_________________________________________________________________\n",
      "bidirectional_9 (Bidirection (None, 113, 120)          58320     \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_9 (Glob (None, 120)               0         \n",
      "_________________________________________________________________\n",
      "dense_128 (Dense)            (None, 50)                6050      \n",
      "_________________________________________________________________\n",
      "dropout_73 (Dropout)         (None, 50)                0         \n",
      "_________________________________________________________________\n",
      "dense_129 (Dense)            (None, 12)                612       \n",
      "=================================================================\n",
      "Total params: 193,182\n",
      "Trainable params: 185,082\n",
      "Non-trainable params: 8,100\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Counting No. of Parameters in Deep Learning Models by Hand\n",
    "\n",
    "https://towardsdatascience.com/counting-no-of-parameters-in-deep-learning-models-by-hand-8f1716241889\n",
    "\n",
    "\"\n",
    "\n",
    "\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot validation performance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 565,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_val = model.predict(x_valid_seq)\n",
    "y_pred_val = y_pred_val.argmax(axis=-1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 566,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_valid, y_pred_val)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.83      0.84        98\n",
      "           1       0.84      0.67      0.74       678\n",
      "           2       0.75      0.71      0.72       282\n",
      "           3       0.72      0.78      0.75       819\n",
      "           4       0.86      0.82      0.84       460\n",
      "           5       0.96      0.84      0.89       469\n",
      "           6       0.75      0.78      0.76       841\n",
      "           7       0.72      0.89      0.80       965\n",
      "           8       0.87      0.85      0.86       742\n",
      "           9       0.90      0.57      0.70       187\n",
      "          10       0.65      0.69      0.67       658\n",
      "          11       0.75      0.64      0.69       304\n",
      "\n",
      "    accuracy                           0.78      6503\n",
      "   macro avg       0.80      0.75      0.77      6503\n",
      "weighted avg       0.78      0.78      0.78      6503\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_valid, y_pred_val))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 568,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dict['cnn_gru_fasttext'].save('cnn_gru_fasttext_{}.h5'.format(epochs))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transformer (TODO)\n",
    "\n",
    "**Reference**\n",
    "\n",
    "* Text classification with transformers in Tensorflow 2: BERT, XLNet\n",
    "\n",
    "https://atheros.ai/blog/text-classification-with-transformers-in-tensorflow-2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Experiments show that the CNN+GRU architecture outperforms the other architectures.\n",
    "Due to the limited time available and the limited computation resources \n",
    "(the experiments were conducted on a Macbook pro 2015), \n",
    "I only trained the model with 60 epochs.\n",
    "The learning curve shows that if I increase the number of epochs, the accuracy might be higher.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the model on the whole trainig set\n",
    "\n",
    "TODO: Train the model with checkpoints\n",
    "\n",
    "- How to Check-Point Deep Learning Models in Keras\n",
    "\n",
    "https://machinelearningmastery.com/check-point-deep-learning-models-keras/\n",
    "\n",
    "https://stackoverflow.com/questions/45393429/keras-how-to-save-model-and-continue-training\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 60\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: debug this function\n",
    "def train_final_model(model, \n",
    "                      x_train, \n",
    "                      y_train,\n",
    "                      model_name,\n",
    "                      epochs,\n",
    "                      batch_size,\n",
    "                      epochs_per_step = 5,\n",
    "                     ):\n",
    "    \"\"\"\n",
    "    train a model with checkpoints\n",
    "    \"\"\"\n",
    "    for n_steps in range(epochs // epochs_per_step):\n",
    "        print('{}/{}'.format(str(n_steps + 1), str(epochs // epochs_per_step)))\n",
    "\n",
    "        filepath = '{}_{}.h5'.format(model_name, str((n_steps + 1) * epochs_per_step))\n",
    "        checkpoint = ModelCheckpoint(filepath=filepath,\n",
    "                                     #save_weights_only=True,\n",
    "                                     monitor='loss',\n",
    "                                     mode='max',\n",
    "                                     save_best_only=True,\n",
    "                                    )\n",
    "        callbacks_list = [checkpoint]\n",
    "\n",
    "        # load the previous model\n",
    "        if n_steps > 0:\n",
    "            prev_filepath = '{}_{}.h5'.format(model_name, str(n_steps * epochs_per_step))\n",
    "            model = load_model(prev_filepath)\n",
    "\n",
    "        history = model.fit(x=x_train, \n",
    "                            y=y_train, \n",
    "                            batch_size=batch_size, \n",
    "                            epochs=epochs_per_step, \n",
    "                            verbose=1,\n",
    "                            callbacks=callbacks_list,\n",
    "                            #validation_data=(x_valid_seq, y_valid_en),\n",
    "                            #callbacks=[earlystop],\n",
    "                           )\n",
    "        #assert_allclose(model.predict(x_train_all_seq[:100]),\n",
    "        #                new_model.predict(x_train_all_seq[:100]),\n",
    "        #                1e-5)\n",
    "\n",
    "        #plot_learning_curves(history)\n",
    "    return model\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/60\n",
      "1017/1017 [==============================] - 255s 251ms/step - loss: 2.3012 - accuracy: 0.1731\n",
      "Epoch 2/60\n",
      "1017/1017 [==============================] - 245s 241ms/step - loss: 1.9207 - accuracy: 0.3068\n",
      "Epoch 3/60\n",
      "1017/1017 [==============================] - 244s 240ms/step - loss: 1.6890 - accuracy: 0.3839\n",
      "Epoch 4/60\n",
      "1017/1017 [==============================] - 243s 239ms/step - loss: 1.3852 - accuracy: 0.4858\n",
      "Epoch 5/60\n",
      "1017/1017 [==============================] - 247s 243ms/step - loss: 1.2698 - accuracy: 0.5367\n",
      "Epoch 6/60\n",
      "1017/1017 [==============================] - 281s 276ms/step - loss: 1.1807 - accuracy: 0.5745\n",
      "Epoch 7/60\n",
      "1017/1017 [==============================] - 224s 220ms/step - loss: 1.1084 - accuracy: 0.6038\n",
      "Epoch 8/60\n",
      "1017/1017 [==============================] - 246s 242ms/step - loss: 1.0632 - accuracy: 0.6197\n",
      "Epoch 9/60\n",
      "1017/1017 [==============================] - 241s 237ms/step - loss: 1.0100 - accuracy: 0.6415\n",
      "Epoch 10/60\n",
      "1017/1017 [==============================] - 249s 245ms/step - loss: 0.9721 - accuracy: 0.6561\n",
      "Epoch 11/60\n",
      "1017/1017 [==============================] - 264s 260ms/step - loss: 0.9395 - accuracy: 0.6677\n",
      "Epoch 12/60\n",
      "1017/1017 [==============================] - 275s 270ms/step - loss: 0.9147 - accuracy: 0.6799\n",
      "Epoch 13/60\n",
      "1017/1017 [==============================] - 245s 241ms/step - loss: 0.8929 - accuracy: 0.6902\n",
      "Epoch 14/60\n",
      "1017/1017 [==============================] - 276s 271ms/step - loss: 0.8796 - accuracy: 0.6910\n",
      "Epoch 15/60\n",
      "1017/1017 [==============================] - 226s 223ms/step - loss: 0.8461 - accuracy: 0.7029\n",
      "Epoch 16/60\n",
      "1017/1017 [==============================] - 236s 232ms/step - loss: 0.8301 - accuracy: 0.7113\n",
      "Epoch 17/60\n",
      "1017/1017 [==============================] - 285s 280ms/step - loss: 0.8134 - accuracy: 0.7183\n",
      "Epoch 18/60\n",
      "1017/1017 [==============================] - 231s 227ms/step - loss: 0.8034 - accuracy: 0.7206\n",
      "Epoch 19/60\n",
      "1017/1017 [==============================] - 252s 248ms/step - loss: 0.7844 - accuracy: 0.7254\n",
      "Epoch 20/60\n",
      "1017/1017 [==============================] - 255s 251ms/step - loss: 0.7703 - accuracy: 0.7330\n",
      "Epoch 21/60\n",
      "1017/1017 [==============================] - 262s 257ms/step - loss: 0.7655 - accuracy: 0.7324\n",
      "Epoch 22/60\n",
      "1017/1017 [==============================] - 245s 241ms/step - loss: 0.7541 - accuracy: 0.7362\n",
      "Epoch 23/60\n",
      "1017/1017 [==============================] - 242s 238ms/step - loss: 0.7416 - accuracy: 0.7425\n",
      "Epoch 24/60\n",
      "1017/1017 [==============================] - 244s 240ms/step - loss: 0.7288 - accuracy: 0.7463\n",
      "Epoch 25/60\n",
      "1017/1017 [==============================] - 251s 247ms/step - loss: 0.7197 - accuracy: 0.7498\n",
      "Epoch 26/60\n",
      "1017/1017 [==============================] - 229s 225ms/step - loss: 0.7194 - accuracy: 0.7504\n",
      "Epoch 27/60\n",
      "1017/1017 [==============================] - 248s 243ms/step - loss: 0.7096 - accuracy: 0.7527\n",
      "Epoch 28/60\n",
      "1017/1017 [==============================] - 247s 243ms/step - loss: 0.7041 - accuracy: 0.7563\n",
      "Epoch 29/60\n",
      "1017/1017 [==============================] - 246s 241ms/step - loss: 0.7000 - accuracy: 0.7573\n",
      "Epoch 30/60\n",
      "1017/1017 [==============================] - 241s 237ms/step - loss: 0.6875 - accuracy: 0.7601\n",
      "Epoch 31/60\n",
      "1017/1017 [==============================] - 234s 230ms/step - loss: 0.6850 - accuracy: 0.7602\n",
      "Epoch 32/60\n",
      "1017/1017 [==============================] - 222s 218ms/step - loss: 0.6784 - accuracy: 0.7674\n",
      "Epoch 33/60\n",
      "1017/1017 [==============================] - 217s 213ms/step - loss: 0.6704 - accuracy: 0.7679\n",
      "Epoch 34/60\n",
      "1017/1017 [==============================] - 219s 215ms/step - loss: 0.6600 - accuracy: 0.7700\n",
      "Epoch 35/60\n",
      "1017/1017 [==============================] - 217s 213ms/step - loss: 0.6612 - accuracy: 0.7731\n",
      "Epoch 36/60\n",
      "1017/1017 [==============================] - 220s 217ms/step - loss: 0.6519 - accuracy: 0.7730\n",
      "Epoch 37/60\n",
      "1017/1017 [==============================] - 212s 208ms/step - loss: 0.6541 - accuracy: 0.7754\n",
      "Epoch 38/60\n",
      "1017/1017 [==============================] - 214s 210ms/step - loss: 0.6370 - accuracy: 0.7797\n",
      "Epoch 39/60\n",
      "1017/1017 [==============================] - 247s 243ms/step - loss: 0.6387 - accuracy: 0.7788\n",
      "Epoch 40/60\n",
      "1017/1017 [==============================] - 272s 267ms/step - loss: 0.6335 - accuracy: 0.7818\n",
      "Epoch 41/60\n",
      "1017/1017 [==============================] - 227s 223ms/step - loss: 0.6298 - accuracy: 0.7822\n",
      "Epoch 42/60\n",
      "1017/1017 [==============================] - 246s 241ms/step - loss: 0.6207 - accuracy: 0.7847\n",
      "Epoch 43/60\n",
      "1017/1017 [==============================] - 245s 241ms/step - loss: 0.6228 - accuracy: 0.7837\n",
      "Epoch 44/60\n",
      "1017/1017 [==============================] - 249s 245ms/step - loss: 0.6145 - accuracy: 0.7867\n",
      "Epoch 45/60\n",
      "1017/1017 [==============================] - 219s 215ms/step - loss: 0.6157 - accuracy: 0.7863\n",
      "Epoch 46/60\n",
      "1017/1017 [==============================] - 231s 227ms/step - loss: 0.6093 - accuracy: 0.7884\n",
      "Epoch 47/60\n",
      "1017/1017 [==============================] - 211s 207ms/step - loss: 0.6082 - accuracy: 0.7906\n",
      "Epoch 48/60\n",
      "1017/1017 [==============================] - 233s 229ms/step - loss: 0.6061 - accuracy: 0.7912\n",
      "Epoch 49/60\n",
      "1017/1017 [==============================] - 252s 248ms/step - loss: 0.6028 - accuracy: 0.7913\n",
      "Epoch 50/60\n",
      "1017/1017 [==============================] - 235s 231ms/step - loss: 0.5988 - accuracy: 0.7929\n",
      "Epoch 51/60\n",
      "1017/1017 [==============================] - 231s 227ms/step - loss: 0.6061 - accuracy: 0.7894\n",
      "Epoch 52/60\n",
      "1017/1017 [==============================] - 238s 234ms/step - loss: 0.5894 - accuracy: 0.7947\n",
      "Epoch 53/60\n",
      "1017/1017 [==============================] - 245s 241ms/step - loss: 0.5870 - accuracy: 0.7957\n",
      "Epoch 54/60\n",
      "1017/1017 [==============================] - 220s 216ms/step - loss: 0.5898 - accuracy: 0.7969\n",
      "Epoch 55/60\n",
      "1017/1017 [==============================] - 218s 214ms/step - loss: 0.5923 - accuracy: 0.7964\n",
      "Epoch 56/60\n",
      "1017/1017 [==============================] - 232s 228ms/step - loss: 0.5794 - accuracy: 0.7965\n",
      "Epoch 57/60\n",
      "1017/1017 [==============================] - 242s 238ms/step - loss: 0.5798 - accuracy: 0.8008\n",
      "Epoch 58/60\n",
      "1017/1017 [==============================] - 238s 234ms/step - loss: 0.5844 - accuracy: 0.7977\n",
      "Epoch 59/60\n",
      "1017/1017 [==============================] - 224s 221ms/step - loss: 0.5800 - accuracy: 0.7987\n",
      "Epoch 60/60\n",
      "1017/1017 [==============================] - 246s 242ms/step - loss: 0.5783 - accuracy: 0.8011\n"
     ]
    }
   ],
   "source": [
    "#model = define_gru_model(word_index=word_index, \n",
    "#                         max_sent_len=max_sent_len,\n",
    "#                         embedding_matrix=fasttext_embedding_matrix,\n",
    "#                        )\n",
    "#model_name = 'gru_final'\n",
    "\n",
    "#model = define_cnn_model(word_index=word_index, \n",
    "#                         max_sent_len=max_sent_len,\n",
    "#                         embedding_matrix=fasttext_embedding_matrix,\n",
    "#                        )\n",
    "#model_name = 'cnn_final'\n",
    "\n",
    "model = define_cnn_gru_model(word_index=word_index, \n",
    "                             max_sent_len=max_sent_len,\n",
    "                             embedding_matrix=fasttext_embedding_matrix,\n",
    "                            )\n",
    "model_name = 'cnn_gru_final'\n",
    "\n",
    "#earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=0, mode='auto')\n",
    "history = model.fit(x=x_train_all_seq, \n",
    "                    y=y_train_all_en, \n",
    "                    batch_size=batch_size, \n",
    "                    epochs=epochs, \n",
    "                    verbose=1, \n",
    "#                    #validation_data=(x_valid_seq, y_valid_en),\n",
    "#                    #callbacks=[earlystop],\n",
    "                   )\n",
    "\n",
    "#model = train_final_model(model=model, \n",
    "#                          x_train=x_train_all_seq, \n",
    "#                          y_train=y_train_all_en,\n",
    "#                          model_name=model_name,\n",
    "#                          epochs=epochs,\n",
    "#                          batch_size=batch_size,\n",
    "#                          epochs_per_step=5,\n",
    "#                         )\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_17\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_9 (InputLayer)         [(None, 452)]             0         \n",
      "_________________________________________________________________\n",
      "embedding_59 (Embedding)     (None, 452, 300)          8100      \n",
      "_________________________________________________________________\n",
      "conv1d_44 (Conv1D)           (None, 452, 100)          120100    \n",
      "_________________________________________________________________\n",
      "max_pooling1d_44 (MaxPooling (None, 113, 100)          0         \n",
      "_________________________________________________________________\n",
      "bidirectional_8 (Bidirection (None, 113, 120)          58320     \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_8 (Glob (None, 120)               0         \n",
      "_________________________________________________________________\n",
      "dense_120 (Dense)            (None, 50)                6050      \n",
      "_________________________________________________________________\n",
      "dropout_68 (Dropout)         (None, 50)                0         \n",
      "_________________________________________________________________\n",
      "dense_121 (Dense)            (None, 12)                612       \n",
      "=================================================================\n",
      "Total params: 193,182\n",
      "Trainable params: 185,082\n",
      "Non-trainable params: 8,100\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dict['{}_fasttext'.format(model_name)] = model\n",
    "model_dict['{}_fasttext'.format(model_name)].save('{}_fasttext_{}.h5'.format(model_name, epochs))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Increase the number of epochs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 602,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 80\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 603,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/80\n",
      "1017/1017 [==============================] - 270s 265ms/step - loss: 2.3032 - accuracy: 0.1687\n",
      "Epoch 2/80\n",
      "1017/1017 [==============================] - 221s 217ms/step - loss: 1.7840 - accuracy: 0.3227\n",
      "Epoch 3/80\n",
      "1017/1017 [==============================] - 214s 210ms/step - loss: 1.4864 - accuracy: 0.4553\n",
      "Epoch 4/80\n",
      "1017/1017 [==============================] - 255s 250ms/step - loss: 1.3547 - accuracy: 0.5040\n",
      "Epoch 5/80\n",
      "1017/1017 [==============================] - 231s 227ms/step - loss: 1.2749 - accuracy: 0.5334\n",
      "Epoch 6/80\n",
      "1017/1017 [==============================] - 227s 223ms/step - loss: 1.2045 - accuracy: 0.5621\n",
      "Epoch 7/80\n",
      "1017/1017 [==============================] - 295s 290ms/step - loss: 1.1447 - accuracy: 0.5859\n",
      "Epoch 8/80\n",
      "1017/1017 [==============================] - 267s 263ms/step - loss: 1.0896 - accuracy: 0.6060\n",
      "Epoch 9/80\n",
      "1017/1017 [==============================] - 273s 268ms/step - loss: 1.0362 - accuracy: 0.6268\n",
      "Epoch 10/80\n",
      "1017/1017 [==============================] - 233s 229ms/step - loss: 0.9961 - accuracy: 0.6444\n",
      "Epoch 11/80\n",
      "1017/1017 [==============================] - 242s 238ms/step - loss: 0.9618 - accuracy: 0.6578\n",
      "Epoch 12/80\n",
      "1017/1017 [==============================] - 256s 252ms/step - loss: 0.9398 - accuracy: 0.6656\n",
      "Epoch 13/80\n",
      "1017/1017 [==============================] - 276s 272ms/step - loss: 0.9129 - accuracy: 0.6757\n",
      "Epoch 14/80\n",
      "1017/1017 [==============================] - 272s 268ms/step - loss: 0.8996 - accuracy: 0.6808\n",
      "Epoch 15/80\n",
      "1017/1017 [==============================] - 300s 295ms/step - loss: 0.8835 - accuracy: 0.6888\n",
      "Epoch 16/80\n",
      "1017/1017 [==============================] - 325s 319ms/step - loss: 0.8631 - accuracy: 0.6945\n",
      "Epoch 17/80\n",
      "1017/1017 [==============================] - 266s 262ms/step - loss: 0.8486 - accuracy: 0.7018\n",
      "Epoch 18/80\n",
      "1017/1017 [==============================] - 267s 262ms/step - loss: 0.8333 - accuracy: 0.7063\n",
      "Epoch 19/80\n",
      "1017/1017 [==============================] - 336s 331ms/step - loss: 0.8267 - accuracy: 0.7113\n",
      "Epoch 20/80\n",
      "1017/1017 [==============================] - 324s 319ms/step - loss: 0.8175 - accuracy: 0.7142\n",
      "Epoch 21/80\n",
      "1017/1017 [==============================] - 279s 274ms/step - loss: 0.8016 - accuracy: 0.7149\n",
      "Epoch 22/80\n",
      "1017/1017 [==============================] - 263s 259ms/step - loss: 0.7930 - accuracy: 0.7206\n",
      "Epoch 23/80\n",
      "1017/1017 [==============================] - 273s 269ms/step - loss: 0.7823 - accuracy: 0.7227\n",
      "Epoch 24/80\n",
      "1017/1017 [==============================] - 316s 311ms/step - loss: 0.7723 - accuracy: 0.7289\n",
      "Epoch 25/80\n",
      "1017/1017 [==============================] - 317s 312ms/step - loss: 0.7685 - accuracy: 0.7326\n",
      "Epoch 26/80\n",
      "1017/1017 [==============================] - 268s 263ms/step - loss: 0.7628 - accuracy: 0.7319\n",
      "Epoch 27/80\n",
      "1017/1017 [==============================] - 288s 283ms/step - loss: 0.7501 - accuracy: 0.7364\n",
      "Epoch 28/80\n",
      "1017/1017 [==============================] - 269s 264ms/step - loss: 0.7498 - accuracy: 0.7377\n",
      "Epoch 29/80\n",
      "1017/1017 [==============================] - 242s 238ms/step - loss: 0.7389 - accuracy: 0.7393\n",
      "Epoch 30/80\n",
      "1017/1017 [==============================] - 257s 252ms/step - loss: 0.7309 - accuracy: 0.7427\n",
      "Epoch 31/80\n",
      "1017/1017 [==============================] - 263s 258ms/step - loss: 0.7313 - accuracy: 0.7450\n",
      "Epoch 32/80\n",
      "1017/1017 [==============================] - 258s 254ms/step - loss: 0.7214 - accuracy: 0.7469\n",
      "Epoch 33/80\n",
      "1017/1017 [==============================] - 261s 257ms/step - loss: 0.7195 - accuracy: 0.7485\n",
      "Epoch 34/80\n",
      "1017/1017 [==============================] - 254s 250ms/step - loss: 0.7129 - accuracy: 0.7523\n",
      "Epoch 35/80\n",
      "1017/1017 [==============================] - 252s 248ms/step - loss: 0.7103 - accuracy: 0.7515\n",
      "Epoch 36/80\n",
      "1017/1017 [==============================] - 241s 237ms/step - loss: 0.7026 - accuracy: 0.7527\n",
      "Epoch 37/80\n",
      "1017/1017 [==============================] - 238s 234ms/step - loss: 0.6983 - accuracy: 0.7558\n",
      "Epoch 38/80\n",
      "1017/1017 [==============================] - 241s 237ms/step - loss: 0.6921 - accuracy: 0.7594\n",
      "Epoch 39/80\n",
      "1017/1017 [==============================] - 236s 232ms/step - loss: 0.6840 - accuracy: 0.7607\n",
      "Epoch 40/80\n",
      "1017/1017 [==============================] - 247s 243ms/step - loss: 0.6842 - accuracy: 0.7598\n",
      "Epoch 41/80\n",
      "1017/1017 [==============================] - 241s 236ms/step - loss: 0.6783 - accuracy: 0.7621\n",
      "Epoch 42/80\n",
      "1017/1017 [==============================] - 239s 235ms/step - loss: 0.6724 - accuracy: 0.7626\n",
      "Epoch 43/80\n",
      "1017/1017 [==============================] - 238s 234ms/step - loss: 0.6721 - accuracy: 0.7641\n",
      "Epoch 44/80\n",
      "1017/1017 [==============================] - 237s 233ms/step - loss: 0.6666 - accuracy: 0.7673\n",
      "Epoch 45/80\n",
      "1017/1017 [==============================] - 235s 231ms/step - loss: 0.6630 - accuracy: 0.7700\n",
      "Epoch 46/80\n",
      "1017/1017 [==============================] - 234s 230ms/step - loss: 0.6551 - accuracy: 0.7711\n",
      "Epoch 47/80\n",
      "1017/1017 [==============================] - 258s 253ms/step - loss: 0.6565 - accuracy: 0.7702\n",
      "Epoch 48/80\n",
      "1017/1017 [==============================] - 316s 310ms/step - loss: 0.6550 - accuracy: 0.7704\n",
      "Epoch 49/80\n",
      "1017/1017 [==============================] - 284s 279ms/step - loss: 0.6498 - accuracy: 0.7735\n",
      "Epoch 50/80\n",
      "1017/1017 [==============================] - 264s 260ms/step - loss: 0.6476 - accuracy: 0.7726\n",
      "Epoch 51/80\n",
      "1017/1017 [==============================] - 258s 253ms/step - loss: 0.6452 - accuracy: 0.7769\n",
      "Epoch 52/80\n",
      "1017/1017 [==============================] - 264s 259ms/step - loss: 0.6438 - accuracy: 0.7779\n",
      "Epoch 53/80\n",
      "1017/1017 [==============================] - 268s 263ms/step - loss: 0.6331 - accuracy: 0.7791\n",
      "Epoch 54/80\n",
      "1017/1017 [==============================] - 268s 263ms/step - loss: 0.6389 - accuracy: 0.7752\n",
      "Epoch 55/80\n",
      "1017/1017 [==============================] - 274s 270ms/step - loss: 0.6293 - accuracy: 0.7811\n",
      "Epoch 56/80\n",
      "1017/1017 [==============================] - 259s 255ms/step - loss: 0.6371 - accuracy: 0.7774\n",
      "Epoch 57/80\n",
      "1017/1017 [==============================] - 248s 244ms/step - loss: 0.6277 - accuracy: 0.7815\n",
      "Epoch 58/80\n",
      "1017/1017 [==============================] - 269s 265ms/step - loss: 0.6225 - accuracy: 0.7809\n",
      "Epoch 59/80\n",
      "1017/1017 [==============================] - 297s 292ms/step - loss: 0.6252 - accuracy: 0.7819\n",
      "Epoch 60/80\n",
      "1017/1017 [==============================] - 273s 269ms/step - loss: 0.6156 - accuracy: 0.7861\n",
      "Epoch 61/80\n",
      "1017/1017 [==============================] - 259s 255ms/step - loss: 0.6235 - accuracy: 0.7821\n",
      "Epoch 62/80\n",
      "1017/1017 [==============================] - 278s 273ms/step - loss: 0.6179 - accuracy: 0.7852\n",
      "Epoch 63/80\n",
      "1017/1017 [==============================] - 285s 280ms/step - loss: 0.6128 - accuracy: 0.7852\n",
      "Epoch 64/80\n",
      "1017/1017 [==============================] - 279s 274ms/step - loss: 0.6115 - accuracy: 0.7866\n",
      "Epoch 65/80\n",
      "1017/1017 [==============================] - 275s 270ms/step - loss: 0.6098 - accuracy: 0.7864\n",
      "Epoch 66/80\n",
      "1017/1017 [==============================] - 281s 277ms/step - loss: 0.6136 - accuracy: 0.7852\n",
      "Epoch 67/80\n",
      "1017/1017 [==============================] - 295s 291ms/step - loss: 0.6098 - accuracy: 0.7848\n",
      "Epoch 68/80\n",
      "1017/1017 [==============================] - 266s 262ms/step - loss: 0.6024 - accuracy: 0.7876\n",
      "Epoch 69/80\n",
      "1017/1017 [==============================] - 254s 249ms/step - loss: 0.6137 - accuracy: 0.7853\n",
      "Epoch 70/80\n",
      "1017/1017 [==============================] - 256s 252ms/step - loss: 0.6003 - accuracy: 0.7921\n",
      "Epoch 71/80\n",
      "1017/1017 [==============================] - 266s 261ms/step - loss: 0.6042 - accuracy: 0.7880\n",
      "Epoch 72/80\n",
      "1017/1017 [==============================] - 313s 308ms/step - loss: 0.6029 - accuracy: 0.7884\n",
      "Epoch 73/80\n",
      "1017/1017 [==============================] - 276s 271ms/step - loss: 0.6003 - accuracy: 0.7879\n",
      "Epoch 74/80\n",
      "1017/1017 [==============================] - 321s 316ms/step - loss: 0.5975 - accuracy: 0.7926\n",
      "Epoch 75/80\n",
      "1017/1017 [==============================] - 320s 315ms/step - loss: 0.5907 - accuracy: 0.7957\n",
      "Epoch 76/80\n",
      "1017/1017 [==============================] - 280s 275ms/step - loss: 0.5933 - accuracy: 0.7920\n",
      "Epoch 77/80\n",
      "1017/1017 [==============================] - 300s 295ms/step - loss: 0.5943 - accuracy: 0.7941\n",
      "Epoch 78/80\n",
      "1017/1017 [==============================] - 291s 286ms/step - loss: 0.5921 - accuracy: 0.7945\n",
      "Epoch 79/80\n",
      "1017/1017 [==============================] - 275s 271ms/step - loss: 0.5848 - accuracy: 0.7959\n",
      "Epoch 80/80\n",
      "1017/1017 [==============================] - 292s 287ms/step - loss: 0.5906 - accuracy: 0.7947\n"
     ]
    }
   ],
   "source": [
    "model = define_cnn_gru_model(word_index=word_index, \n",
    "                             max_sent_len=max_sent_len,\n",
    "                             embedding_matrix=fasttext_embedding_matrix,\n",
    "                            )\n",
    "model_name = 'cnn_gru_final'\n",
    "\n",
    "#earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=0, mode='auto')\n",
    "history = model.fit(x=x_train_all_seq, \n",
    "                    y=y_train_all_en, \n",
    "                    batch_size=batch_size, \n",
    "                    epochs=epochs, \n",
    "                    verbose=1, \n",
    "#                    #validation_data=(x_valid_seq, y_valid_en),\n",
    "#                    #callbacks=[earlystop],\n",
    "                   )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 604,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dict['{}_fasttext_{}'.format(model_name, epochs)] = model\n",
    "model_dict['{}_fasttext_{}'.format(model_name, epochs)].save('{}_fasttext_{}.h5'.format(model_name, epochs))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 605,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_21\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_11 (InputLayer)        [(None, 452)]             0         \n",
      "_________________________________________________________________\n",
      "embedding_64 (Embedding)     (None, 452, 300)          8100      \n",
      "_________________________________________________________________\n",
      "conv1d_48 (Conv1D)           (None, 452, 100)          120100    \n",
      "_________________________________________________________________\n",
      "max_pooling1d_48 (MaxPooling (None, 113, 100)          0         \n",
      "_________________________________________________________________\n",
      "bidirectional_10 (Bidirectio (None, 113, 120)          58320     \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_10 (Glo (None, 120)               0         \n",
      "_________________________________________________________________\n",
      "dense_130 (Dense)            (None, 50)                6050      \n",
      "_________________________________________________________________\n",
      "dropout_74 (Dropout)         (None, 50)                0         \n",
      "_________________________________________________________________\n",
      "dense_131 (Dense)            (None, 12)                612       \n",
      "=================================================================\n",
      "Total params: 193,182\n",
      "Trainable params: 185,082\n",
      "Non-trainable params: 8,100\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_dict['{}_fasttext_{}'.format(model_name, epochs)].summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make the prediction on the test set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 606,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = 'cnn_gru_final'\n",
    "embedding_name = 'fasttext'\n",
    "#epochs = 60\n",
    "epochs = 80\n",
    "#epochs = 100\n",
    "\n",
    "model = load_model('{}_{}_{}.h5'.format(model_name, embedding_name, epochs))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 607,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predictions = model.predict_classes(x_test_seq)\n",
    "predictions = model.predict(x_test_seq)\n",
    "predictions = predictions.argmax(axis=-1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 608,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3000"
      ]
     },
     "execution_count": 608,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(predictions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 609,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6, 11,  8,  8,  4])"
      ]
     },
     "execution_count": 609,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0:5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 610,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('ytest.txt', 'w') as f:\n",
    "    for pred in predictions:\n",
    "        f.write(\"%s\\n\" % pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 611,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3000\n",
      "[6, 11, 8, 8, 4]\n"
     ]
    }
   ],
   "source": [
    "# show some results in the ytest.txt\n",
    "\n",
    "y_test = []\n",
    "with open('ytest.txt') as f:\n",
    "    y_test = f.read().strip().split('\\n')\n",
    "y_test = [int(y) for y in y_test]\n",
    "print(len(y_test))\n",
    "print(y_test[0:5])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Experiments show that the proposed approach (fastText + CNN + GRU) achieves around 78% accuracy on the validation set. \n",
    "The validation set consists of 20% of the data randomly selected from xtrain_obfuscated.txt.\n",
    "The rest 80% of the data is used as a training set to train the deep learning model.\n",
    "\n",
    "Based on the assumption that the sentences of train and test sets belong to the same distribution and the results of the experiments, \n",
    "I would expect the proposed approach achieves similar accuracy (around 78%) on the test set.\n",
    "\n",
    "Please note, due to the limited time available and the limited computation resources \n",
    "(the experiments were conducted on a Macbook pro 2015), \n",
    "I only trained the model with 80 epochs.\n",
    "The learning curve shows that if I increase the number of epochs, the accuracy might be higher.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Future work\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Error analysis\n",
    "- Hyperparameter tuning\n",
    "- Find the patterns in the obfuscated text\n",
    "- Investigate other feature engineering approach for the obfuscated text\n",
    "- Using transformers\n",
    "- Balance data so that each class has the same amount of samples\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reference\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Deep Convolutional Neural Network for Sentiment Analysis (Text Classification)\n",
    "\n",
    "https://machinelearningmastery.com/develop-word-embedding-model-predicting-movie-review-sentiment/\n",
    "\n",
    "\n",
    "* Practical Text Classification With Python and Keras\n",
    "\n",
    "https://realpython.com/python-keras-text-classification/\n",
    "\n",
    "\n",
    "* What Are Word Embeddings for Text?\n",
    "\n",
    "https://machinelearningmastery.com/what-are-word-embeddings/\n",
    "\n",
    "\n",
    "* GloVe and fastText — Two Popular Word Vector Models in NLP\n",
    "\n",
    "https://dzone.com/articles/glove-and-fasttext-two-popular-word-vector-models\n",
    "\n",
    "\n",
    "* Using pre-trained word embeddings\n",
    "\n",
    "https://keras.io/examples/nlp/pretrained_word_embeddings/\n",
    "\n",
    "\n",
    "* How to Use Word Embedding Layers for Deep Learning with Keras\n",
    "\n",
    "https://machinelearningmastery.com/use-word-embedding-layers-deep-learning-keras/\n",
    "\n",
    "\n",
    "* A Tour of Recurrent Neural Network Algorithms for Deep Learning\n",
    "\n",
    "https://machinelearningmastery.com/recurrent-neural-network-algorithms-for-deep-learning/\n",
    "\n",
    "\n",
    "* How to Develop a Multichannel CNN Model for Text Classification\n",
    "\n",
    "https://machinelearningmastery.com/develop-n-gram-multichannel-convolutional-neural-network-sentiment-analysis/\n",
    "\n",
    "\n",
    "* Tackling Toxic Problem with Char-Gram+CNN+LSTM\n",
    "\n",
    "https://www.kaggle.com/sbongo/tackling-toxic-problem-with-char-gram-cnn-lstm\n",
    "\n",
    "\n",
    "* Best Practices for Text Classification with Deep Learning\n",
    "\n",
    "https://machinelearningmastery.com/best-practices-document-classification-deep-learning/\n",
    "\n",
    "\n",
    "* A Tour of Recurrent Neural Network Algorithms for Deep Learning\n",
    "\n",
    "https://machinelearningmastery.com/recurrent-neural-network-algorithms-for-deep-learning/\n",
    "\n",
    "\n",
    "* How to Check-Point Deep Learning Models in Keras\n",
    "\n",
    "https://machinelearningmastery.com/check-point-deep-learning-models-keras/\n",
    "\n",
    "\n",
    "* Advanced Keras — Accurately Resuming a Training Process\n",
    "\n",
    "https://towardsdatascience.com/resuming-a-training-process-with-keras-3e93152ee11a\n",
    "\n",
    "\n",
    "* Text classification with transformers in Tensorflow 2: BERT, XLNet\n",
    "\n",
    "https://atheros.ai/blog/text-classification-with-transformers-in-tensorflow-2\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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